Herramientas Analíticas en la Clasificación de Mieles en ......La memoria titulada ^Herramientas...
206
Herramientas Analíticas en la Clasificación de Mieles en Base a Criterios de Calidad e Inocuidad TESIS DOCTORAL Presentada por: Marisol Juan Borrás Dirigida por: Dra. Isabel Escriche Roberto Valencia, Febrero de 2016
Transcript of Herramientas Analíticas en la Clasificación de Mieles en ......La memoria titulada ^Herramientas...
Herramientas Analíticas en la
Clasificación de Mieles en Base a
Criterios de Calidad e Inocuidad
TESIS DOCTORAL
Presentada por:
Marisol Juan Borrás
Dirigida por:
Dra. Isabel Escriche Roberto
Valencia, Febrero de 2016
Dra. Isabel Escriche Roberto, Catedrática de Universidad, perteneciente al Departamento de Tecnología de Alimentos de la Universitat Politécnica de Valencia e Investigadora del Instituto de Ingeniería de Alimentos para el Desarrollo de la misma Universidad,
Hace constar que:
La memoria titulada “Herramientas analíticas en la clasificación de mieles en base a criterios de calidad e inocuidad” que presenta Dª Marisol Juan Borrás para optar al grado de Doctor por la Universitat Politécnica de Valencia, ha sido realizada en el Instituto de Ingeniería de Alimentos para el Desarrollo (IuIAD – UPV) bajo su dirección y que reúne las condiciones para ser defendida por su autora.
Valencia, 28 de Diciembre 2015
Fdo. Isabel Escriche Roberto
A mis padres
Agradecimientos
Quiero agradecer en primer lugar a mi directora de Tesis Dra. Isabel Escriche Roberto la
ayuda y el apoyo que me ha prestado para poder llevar a cabo este proyecto. Así como el
trabajo y la lucha que compartimos juntas por una ilusión que empezó hace ya 10 años, y
que aunque ha tropezado con algunos obstáculos sigue adelante.
Quiero dar las gracias a todos los alumnos tanto españoles como extranjeros que han
pasado por el Laboratorio de la Miel, y que en mayor o menor medida, todos han
contribuido con su granito de arena al trabajo que llevamos a cabo en este laboratorio.
Especialmente recuerdo la etapa que compartí con Melinda, mientras realizaba su tesis
doctoral, que tantas cosas compartimos como compañeras y madres que empiezan.
Agradezco de corazón la ayuda que he recibido de mi compañera y amiga Angela, que
también compartió conmigo su camino al realizar su tesis doctoral.
Quiero agradecer a mi compañero Román, su apoyo, consejos, paciencia y masajes
mientras realizaba esta tesis. Así como el esfuerzo cuidando de nuestros hijos y de mí.
Gracias.
Resumen
I
Resumen
La miel, consumida por el hombre desde hace miles de años debido a sus propiedades
organolépticas y terapéuticas, es el producto de la unión entre el mundo animal, la abeja
(Apis melífera), y el vegetal, el néctar de las flores y/o secreciones azucaradas de las
plantas o insectos. Hoy en día las empresas envasadoras y comercializadoras de miel,
para cumplir las exigencias legales y comerciales, deben llevar a cabo una etapa previa al
proceso industrial que consiste en clasificar las mieles que entran en la empresa como
materia prima, tanto en lo que respecta a criterios de calidad como de inocuidad. Entre
los primeros, además de los referentes al cumplimiento de los niveles de los parámetros
fisicoquímicos exigidos por la normativa nacional e internacional, destaca la necesidad de
clasificar las mieles atendiendo al origen botánico, por el consiguiente valor añadido que
implica para las empresas. En este sentido, la búsqueda de nuevas herramientas
analíticas que faciliten la diferenciación botánica de las mieles sería de gran utilidad para
el sector apícola. Esto es debido a que la metodología tradicional basada en la
cuantificación del contenido polínico presenta no solo el inconveniente de requerir
personal experto, sino además el de estar sujeto a interferencias, especialmente cuando
el contenido polínico esté infrarrepresentado, como sucede en algunos tipos de miel.
Otro aspecto esencial para el sector, es el relacionado con la posible presencia en la miel
de residuos químicos (antibióticos o pesticidas) como consecuencia directa de los
tratamientos veterinarios o indirecta de los tratamientos agrícolas. A este respecto,
garantizar el cumplimiento de la legislación y la reducción de riesgos para el consumidor
es un requisito fundamental en el campo de la Seguridad Alimentaria. Para ello, se debe
controlar la materia prima en la recepción industrial, llevando a cabo los análisis
adecuados mediante métodos validados y contrastados.
En este sentido, la presente tesis doctoral se plantea con dos objetivos claramente
diferenciados: 1. Evaluar las técnicas que se vienen utilizando de forma rutinaria en el
control de calidad de mieles, tanto a nivel industrial como comercial, y compararlas con
otras alternativas no convencionales y 2. Evaluar la efectividad del control de la materia
prima (llevado a cabo en la etapa de recepción industrial) para cumplir los límites legales
establecidos en lo referente a la presencia de residuos químicos en miel. Además, valorar
el riesgo para el consumidor como consecuencia de la exposición a dichos residuos
cuando legalmente tenga establecido un LMR (Límite máximo de residuos).
De los resultados obtenidos se concluye que, en general, los parámetros fisicoquímicos,
que se vienen utilizando de forma convencional en la clasificación de mieles, no permiten
una buena diferenciación de las mismas en términos de monofloralidad. Si bien, el origen
botánico de las mieles tiene un claro impacto en algunos de ellos, como es el color y la
conductividad eléctrica, los niveles de ciertos parámetros fisicoquímicos pueden variar
en función del año de recolección (especialmente el color) y de las prácticas apícolas. En
este sentido, el apicultor tiene un importante papel en la variabilidad de algunos de estos
Resumen
II
parámetros, especialmente en lo que respecta al HMF y la humedad, e incluso al color
varietal característico que el mercado requiere. Por lo tanto, unas buenas prácticas
apícolas son vitales para obtener el producto que el consumidor espera y la legislación
exige.
Las técnicas alternativas ensayadas en el presente estudio, como es el caso de la
identificación de compuestos volátiles característicos en la fracción volátil de las mieles,
y la aplicación de un sistema de lengua electrónica construido con sensores metálicos,
han proporcionado resultados útiles y esperanzadores en clasificación de mieles para
complementar la información obtenida mediante el análisis polínico.
La utilización de marcadores químicos, como es el caso del antranilato de metilo en las
mieles de cítrico, resulta especialmente útil cuando éstas presenten un bajo porcentaje
de polen de la especie botánica de la que mayoritariamente procedan (por variedades
híbridas estériles o producción de polen y néctar no simultánea). Sin embargo,
inexplicablemente las transacciones comerciales son, en ocasiones más exigentes con
esta variedad de miel que con otras, ya que no solo exigen la presencia de un porcentaje
de polen mínimo de Citrus spp. (10-20%), sino que además requieren un nivel de
antranilato de metilo no inferior a 2 mg/kg. En este sentido, la presente tesis doctoral
propone reconsiderar la concentración requerida de este compuesto para la miel de
cítricos españoles, a un valor mínimo de 1.2 mg/kg (superior al sugerido en otros estudios
para mieles de cítrico italianas), y además sólo tener en cuenta este parámetro en el caso
de las mieles con un sorprendente bajo porcentaje de polen de cítricos, y después de la
evaluación de sus propiedades organolépticas y fisicoquímicas.
La presencia de determinados compuestos, en la fracción volátil de las mieles, resulta
determinante en su diferenciación; siendo el origen botánico el que mayor influencia
tiene en su discriminación y en menor medida el origen geográfico. Por ejemplo,
carvacrol y -terpineno son característicos de la miel de tilo; -pineno y 3-methyl-2-
butanol de la miel de girasol, y óxido de cis-linalool de la miel de acacia.
Con relación a la lengua electrónica, construida con sensores metálicos, la combinación
de la información con ella generada junto con la aplicación de adecuadas técnicas
estadísticas multivariantes (Análisis de Componentes Principales y Redes Neuronales) ha
demostrado que este sistema permite la diferenciación de mieles según su origen
botánico con un porcentaje de éxito del 100%. Además, se ha confirmado una buena
correlación entre la lengua electrónica y la capacidad antioxidante de las mieles (0.9666).
En lo que respecta al control de residuos químicos, los resultados confirman que un
control de calidad apropiado en la recepción de la materia prima, aplicando una
metodología analítica adecuada y validada, resulta eficaz para reducir en la miel
comercializada el riesgo de exposición por la presencia de sulfonamidas. En este sentido,
se puede considerar que está garantizada la seguridad del consumidor de miel en lo que
respecta no solo a la presencia de sulfamidas, sino también de otras sustancias químicas
Resumen
III
como antibióticos y pesticidas, ya que el control que las empresas llevan a cabo de forma
rutinaria los engloba a todos.
En relación al riesgo de exposición a pesticidas a través del consumo de miel, se concluye
que, aunque en el estudio llevado a cabo en muestras comerciales, no se superó el Límite
Máximo de Residuos (LMR) para ninguno de los pesticidas analizados, el consumidor está
expuesto a muchos de ellos a concentraciones inferiores a dichos límites (especialmente
para los acaricidas destinados al tratamiento de la varroa). Sin embargo, el “Indice de
Peligro” (Hazard Index: HI) para la presencia de pesticidas en las mieles obtenido como
sumatorio del peligro individual de cada pesticida (Hazard Quotient: HQ) presente en
ellas, fue en el peor de los casos 500 veces inferior al valor de 1, considerado como límite
de aceptabilidad. Aunque los consumidores no están expuestos a niveles tóxicos de
pesticidas a través del consumo de miel, sin embargo, siguiendo el principio de que una
exposición tiene que ser “tan baja como sea razonablemente posible”, el sector primario,
apicultores y agricultores, debe hacer un mayor esfuerzo ya que sus prácticas pueden
influir directamente en el problema de la presencia de residuos en la miel.
Resumen
V
Resum
La mel, consumida per l'home des de fa milers d'anys degut a les seues propietats
organolèptiques i terapèutiques, és el producte de la unió entre el món animal, l'abella
(Apis mel·lífera), i el vegetal, el nèctar de les flors y/o secrecions ensucrades de les plantes
o insectes. Hui en dia les empreses envasadores i comercialitzadores de mel, per a
complir les exigències legals i comercials, han de dur a terme una etapa prèvia al procés
industrial que consistix a classificar les mels que entren en l'empresa com a matèria
primera, tant pel que fa a criteris de qualitat com d'innocuïtat. Entre els primers, a més
dels referents al compliment dels nivells dels paràmetres fisicoquímics exigits per la
normativa nacional i internacional, destaca la necessitat de classificar les mels atenent a
l'origen botànic, pel consegüent valor afegit que implica per a les empreses. En este
sentit, la busca de noves ferramentes analítiques que faciliten la diferenciació botànica
de les mels seria de gran utilitat per al sector apícola; ja que la metodologia tradicional
basada en la quantificació del contingut pol·línic presenta no sols l'inconvenient de
requerir personal expert, sinó a més el d'estar subjecte a interferències, especialment
quan el contingut pol·línic estiga infra-representat, com succeïx en alguns tipus de mel.
Un altre aspecte essencial per al sector, és el relacionat amb la possible presència en la
mel de residus químics (antibiòtics o pesticides) com a conseqüència directa dels
tractaments veterinaris o indirecta dels tractaments agrícoles. A este respecte, garantir
el compliment de la legislació i la reducció de riscos per al consumidor és un requisit
fonamental en matèria de Seguretat Alimentària. Per a això, s'ha de controlar la matèria
primera en la recepció industrial, duent a terme les anàlisis adequats per mitjà de
mètodes validats i contrastats.
En este sentit, la present tesi doctoral es planteja amb dos objectius clarament
diferenciats: 1. Avaluar les tècniques que s'utilitzen de forma rutinària en el control de
qualitat de mels, tant a nivell industrial com comercial, i comparar-les amb altres
alternatives no convencionals i 2. Avaluar l'efectivitat del control de la matèria primera
(dut a terme en l'etapa de recepció industrial) per a complir els límits legals establits pel
que fa a la presència de residus químics en mel. A més, valorar el risc per al consumidor
com a conseqüència de l'exposició als anomenats residus quan legalment tinguen establit
un LMR (Límit màxim de residus).
Dels resultats obtinguts es conclou que, en general, els paràmetres fisicoquímics, que
s'utilitzen de forma convencional en la classificació de mels, no permeten una bona
diferenciació de les mateixes en termes de monofloralidat. Si bé, l'origen botànic de les
mels té un clar impacte en alguns d'ells, com és el color i la conductivitat elèctrica, els
nivells de certs paràmetres fisicoquímics poden variar en funció de l'any de recol·lecció
(especialment el color) i de les pràctiques apícoles.En este sentit, l'apicultor té un
important paper en la variabilitat d'alguns d'estos paràmetres, especialment pel que fa al
HMF i la humitat, i inclús al color varietal característic que el mercat requerix. Per tant,
Resumen
VI
unes bones pràctiques apícoles són vitals per a obtindre el producte que el consumidor
espera i la legislació exigix.
Les tècniques alternatives assajades en el present estudi, com és el cas de la identificació
de compostos volàtils característics en la fracció volàtil de les mels, i l'aplicació d'un
sistema de llengua electrònica construït amb sensors metàl·lics, han proporcionat
resultats útils i esperançadors en classificació de mels per a complementar la informació
obtinguda per mitjà de l'anàlisi pol·línica.
La utilització de marcadors químics, com és el cas de l'antranilato de metil en les mels de
cítric, resulta especialment útil quan estes presenten un baix percentatge de pol·len de
l'espècie botànica de què majoritàriament procedisquen (per varietats híbrides estèrils o
producció de pol·len i nèctar no simultània). No obstant això, inexplicablement les
transaccions comercials són, de vegades més exigents amb esta varietat de mel que amb
altres, ja que no sols exigixen la presència d'un percentatge de pol·len mínim de Citrus
spp. (mínim 10-20%), sinó que a més requerixen un mínim contingut d'antranilato de
metil (2 mg/kg) .En este sentit, la present tesi doctoral proposa reconsiderar el nivell de
MA requerit per a la mel espanyola de cítrics, a un valor mínim de 1.2 mg/kg (superior al
suggerit en altres estudis per a mels de cítric italianes), i a més només tindre en compte
este paràmetre en el cas de les mels amb un sorprenent baix percentatge de pol·len de
cítrics, i després de l'avaluació de les seues propietats organolèptiques i fisicoquímiques.
La presència de determinats compostos, en la fracció volàtil de les mels, resulta
determinant en la seva diferenciació; sent l'origen botànic el que major influència té en
la discriminació i en menor mesurada l'origen geogràfic. Per exemple, carvacrol i -
terpineno són característics de la mel de til·ler; -pineno i 3-methyl-2-butanol de la mel
de girasol, i òxid de cis-linalool de la mel d'acàcia.En relació amb la llengua electrònica,
construïda amb sensors metàl·lics, la combinació de la informació amb ella generada
juntament amb l'aplicació d'adequades tècniques estadístiques multivariant (Anàlisis de
Components Principals i Xarxes Neuronals) ha demostrat que aquest sistema permet la
diferenciació de mels segons el seu origen botànic amb un percentatge d'èxit del 100%.
A més, s'ha confirmat una bona correlació entre la llengua electrònica i la capacitat
antioxidant de les mels (0.9666).
Pel que fa al control de residus químics, els resultats confirmen que un control de qualitat
apropiat en la recepció de la matèria primera, aplicant una metodologia analítica
adequada i validada, resulta eficaç per reduir en la mel comercialitzada el risc d'exposició
per la presència de sulfonamides. En aquest sentit, es pot considerar que està garantida
la seguretat del consumidor de mel en el que respecta no tan sols a la presència de
sulfamides, sinó també d'altres substàncies químiques com a antibiòtics i pesticides, ja
que el control que les empreses duen a terme de forma rutinària els engloba a tots.
En relació al risc d'exposició a pesticides a través del consum de mel, es conclou que,
encara que en l'estudi dut a terme en mostres comercials, no es va superar el Límit Màxim
Resumen
VII
de Residus (LMR) para cap dels pesticides analitzats, el consumidor està exposat a molts
d'ells a concentracions inferiors a aquests límits (especialment per als acaricides destinats
al tractament de la varroa). No obstant això, el “Indice de Perill” (Hazard Index: HI) per a
la presència de pesticides en les mels, obtingut com a sumatori del perill individual de
cada pesticida (Hazard Quotient: HQ) present en elles, va anar en el pitjor dels casos 500
vegades inferior al valor d'1, considerat com a límit d'acceptabilitat. Encara que els
consumidors no estan exposats a nivells tòxics de pesticides a través del consum de mel,
no obstant això, seguint el principi que una exposició ha de ser “tan baixa com sigui
raonablement possible”, el sector primari, apicultors i agricultors, ha de fer un esforç ja
que les seves pràctiques poden influir directament en el problema de la presència de
residus en la mel.
Resumen
IX
Abstract
Honey, consumed by people for thousands of years due to its organoleptic and
therapeutic properties, is the product of the union of two worlds, the animal (the bee
Apis mellifera), and the plant (nectar from flowers and/or sweet secretions from plants
and insects). Today, the honey packaging and retail business must classify honey before
the manufacturing process, taking into account quality and safety criteria in order to
meet legal and commercial requirements. In addition to the requirements relating to
compliance with the levels of physico-chemical parameters obligatory by national and
international law, the quality criteria require classification according to botanical origin,
which results in added value for the companies. In this regard, looking for new analytical
tools that facilitate the botanical differentiation of honey would be useful for the
beekeeping sector. This is because the traditional method based on the quantification of
the pollen content not only has the disadvantage of requiring skilled technicians, but is
also sometimes subject to interference, especially when the pollen content is
underrepresented, as occurs in some types of honey.
Another essential aspect for the sector, is that related to the possible presence of
chemical residues (antibiotics or pesticides) in honey, as a direct consequence of
veterinary treatments or indirectly due to agricultural treatments. In this regard,
guaranteeing compliance with the legislation and reducing risks to consumers is an
essential requirement in the field of food safety. For this reason, on receiving batches of
raw honey, the packaging industry must conduct proper analysis using proven and
validated methods.
Taking this into account, the present PhD thesis has two distinct objectives: 1. To evaluate
the techniques that have been used routinely in the quality control of honey, at both an
industrial and commercial level, and to compare them with other unconventional
alternatives, and 2. To evaluate the effectiveness of monitoring the raw material in the
packaging industry (carried out on receiving batches of raw honey,) to meet the legal
limits regarding the presence of chemical residues. Also, to assess the risk to the
consumer as a result of exposure to such residues when there is a legally established
maximum residue limit (MRLs).
Based on the results obtained it is concluded that, in general, the physicochemical
parameters that have been used conventionally in the classification of honey do not
permit good differentiation in terms of monoflorality. While the botanical origin of honey
has a clear impact on some of them, such as the color and electrical conductivity, levels
of certain physicochemical parameters may vary depending on the year of harvest
(especially color) and beekeeping practices. In this line, the beekeeper has an important
role in the variability of some of these parameters, especially in regard to HMF and
moisture, and even in the characteristic varietal color that the market requires.
Resumen
X
Therefore, good beekeeping practices are essential to obtain the product that the
consumer expects and legislation requires.
The alternative techniques tested in this study, such as identifying characteristic volatile
compounds in the volatile fraction of honey, and the application of an electronic tongue
made with metals, have provided useful and promising results in the classification of
honey to complement the information obtained by pollen analysis.
The use of chemical fingerprinting, such as for methyl anthranilate in citrus blossom
honey, is particularly useful when the percentage of pollen is particularly low, as in the
case of sterile hybrids or when pollen and nectar production is not simultaneous.
However, inexplicably commercial transactions are sometimes more demanding with this
type of honey than with others, as they not only require the presence of a minimum
percentage of Citrus spp. pollen (at least 10-20%), but also require a minimum presence
of methyl anthranilate (2 mg/kg). This PhD thesis suggests reconsidering the level of this
compound required in Spanish citrus honey; proposing a minimum value of 1.2 mg/kg
(greater than that recommended in other studies for Italian citrus honey). However, only
taking this parameter into consideration in the case of honey with a surprising low
percentage of citrus pollen, and after evaluating its organoleptic and physicochemical
properties.
The presence of certain compounds, in the volatile fraction of the honey, is decisive in its
differentiation; botanical origin having the greatest influence on discrimination and to a
lesser extent the geographical origin. For example, carvacrol and -terpinene are
characteristic of tilia honey; -pinene and 3-methyl-2-butanol of sunflower honey; and
cis-linalool oxide of acacia honey.
The information obtained with an electronic tongue (made with metal sensors) in
combination with appropriate multivariate statistical techniques (Principal Component
Analysis and Neural Networks) has demonstrated that this system allows the
differentiation of honey by botanical origin with a success rate of 100%. A good
correlation between the electronic tongue and the antioxidant capacity of honey has also
been confirmed (0.9666).
With regard to the control of chemical residues, the results confirm that proper quality
control on receiving batches of raw honey, applying appropriate validated analytical
methods, is effective in reducing the risk of exposure to sulfonamides in commercialized
honey. In this respect, it can be considered that honey consumer safety, with regard not
only to the presence of sulfonamides, but also to other chemical residues such as
antibiotics and pesticides is guaranteed, as the control that companies carry out routinely
covers all this aspects.
Regarding the risk of exposure to pesticides through consumption of honey, it is
concluded that, although in this study, carried out with commercial samples, the
Resumen
XI
Maximum Residue Level (MRL) was not exceeded for any of the pesticides analyzed; the
consumer is exposed to many of them at concentrations below these limits (especially
acaricides used against varroa). However, the "hazard index" (HI) for the presence of
pesticides in honey obtained as the addition of the individual risk of each pesticide
(Hazard Quotient: HQ) present in them, was in the worst case 500 times lower than the
value of 1, considered as the limit of acceptability. Although consumers are not exposed
to toxic levels of pesticides through consumption of honey; the principle that exposure
to residues has to be "as low as reasonably possible", means that the primary sector,
beekeepers and farmers, has to strive to improve their practices as these directly
influence the problem of the presence of residues in honey.
Indice
XIII
Indice General
1. Introducción
1.1. La miel como producto…………………………………………………………………………….. 1 1.1.1. Formación de la miel 1 1.1.2. Composición de la miel y su implicación en la calidad 2
Carbohidratos 2 Humedad 5 Enzimas 5 Compuestos aromáticos 7 Antioxidantes en la miel 8 Otros componentes 8
1.2. Marco legislativo…………………………………………………………………………..……..….. 9 1.2.1. Autenticidad y Frescura 11
Métodos analíticos para la detección de adulteraciones en miel 12 1.2.2. Seguridad 13
Residuos químicos y su determinación 13
Control de los residuos 15
Métodos analíticos de control de residuos químicos. Validación 15
1.3. Clasificación industrial de la miel…………………………………………………..…………. 17 1.3.1. Métodos tradicionales en la clasificación de mieles 17
Análisis melisopalinológico 19
Análisis sensorial 20 Análisis físico-químico y color 21
1.3.2. Métodos alternativos en la clasificación de mieles 23 1.4. El sector…………………………..…………………….……………………………………..…………. 27
1.4.1. Producción primaria 27 1.4.2. Procesado de la miel y controles 28 1.4.3. España en el Contexto de Producción y Comercialización de miel 30
1.5. Bibliografía…………………………………………………………….……………………….……….. 31
2. Objetivos
2.1. Objetivo general…………………………………………………………………………..………….. 43
2.2. Objetivos específicos………………………………………………..……………………..………. 43
3. Resultados
3.1. Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper…………………………………………………………………………….……………….…………
49
3.2. Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey………………………………………….…………………….…………………..
65
3.3. Potenciometric tongues with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity……………………………………………………………………………………………………….…..
85
3.4. Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys…………………………………..……….
105
Indice
XIV
3.5. Routine quality control in honey packaging companies as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS……………………………………………………….…………………….…..
129
3.6. Mixture-risk-assessment of pesticide residues in retail polyfloral honey……………………………………………………………………………………………...…………….…
151
4. Conclusiones
4.1. Conclusiones del objetivo general 1…………………………………………………….…… 171
4.2. Conclusiones del objetivo general 2……………………………………………………….… 173
Conclusión general de la tesis…………………………………………………………………………. 176
Indice
XV
Índice de figuras
Figura 1.1.- Abejas depositando miel en el panal……………………………………..……… 2
Figura 1.2.-Reacciones de equilibrio entre la fructosa y el HMF en medio ácido. 1 Fructosa, 2 Fructofuranosa, 3–4 Dos etapas intermedias de deshidratación, 5 HMF……………………………………………………………………………………..
5
Figura 1.3.- Rueda de olor y aroma de la miel (IHC, 2001)……………………………….. 21 Figura 1.4.- Esquema de los componentes que forman la lengua electrónica…….…..… 26 Figura 1.5.-Desoperculado de las celdillas y extracción de miel por centrifugación………………………………………………………………………………………….....…..
27
Figura 1.6.- Diagrama de flujo del procesado de miel……………….………….………….. 29
Figura 1.7.- Producción mundial de miel en 2012 (en T)…………….……….…………… 30 Figura 1.8.- Producción de miel en Europa en 2012 (en T)…………….………………… 31 Figura 3.1.1.- Pictures corresponding to the predominant pollen present in each type of unifloral honey and the minimum percentage of pollen required to classify a honey as belonging to a specific botanical genus considered in the present work……………………………………………………….…………………………………….…….
55
Figura 3.1.2.-Multiple correspondence analyses of the quality parameters coded as intervals with projected varieties of honey. M: moisture; C: colour; HMF: hydroxymethylfurfural……………………………………………..…………………………….
57
Figura 3.2.1.-Box and whisker plots for pollen, MA, HMF, electrical conductivity, moisture and colour for honeys from the bee-keepers (B) and retail outlets (R). Samples harvested in 2011…………………………………………...……...
74
Figura 3.3.1.-PCA biplot (scores and loadings) of the variables (physicochemical parameters, sugars, color and total antioxidant activity) and honey samples: C (orange blossom); R (rosemary); T (thyme); S (sunflower); W (winter savory honey); H (honeydew honey). Next to each sample the percentage of the relevant predominant pollen is shown…………………………….….
94
Figura 3.3.2.-PCA biplot (scores and loadings) from the measurements obtained with the 8 electrodes together (4 noble and 4 non-noble metals: “Au, Pt, Ir, Rh, Ag, Ni, Co and Cu”). C (orange blossom); R (rosemary); T (thyme); S (sunflower); W (winter savory honey); H (honeydew honey)………..…
96
Figura 3.3.3.- PCA biplot (scores and loadings) from the measurements obtained with the 5 electrodes together (4 non-noble metals “Ag, Ni, Co and Cu”, plus Au). C (orange blossom); R (rosemary); T (thyme); S (sunflower); W (winter savory honey); H (honeydew honey)……………………………………………….…..
97
Figura 3.3.4.- Predicted versus actual values of total antioxidant capacity given by the MLR model. A) 8 electrodes: 4 noble metals “Au, Pt, Ir and Rh” plus 4 non-noble metals “Ag, Ni, Co and Cu” and B) 4 non-noble “Ag, Ni, Co and Cu” metals plus Au……………………………………………………………………………………………….…
99
Figura 3.4.1.-Biplot for the two principal components of the PCA model for the physicochemical parameters, sugars (fructose “F”, glucose “G” sucrose “S” and F/G ratio) and colour (Pfund and CIEL*a*b) in acacia, sunflower and tilia honeys harvested in the different countries: Spain (Sp), Romania (Ro), and Czech Republic (Cz)…………………………………………………………………………………..………
116
Indice
XVI
Figura 3.4.2.- Biplot for the two principal components of the PCA model for the volatiles compounds identified in acacia, sunflower and tilia honeys harvested in the different countries: Spain (Sp), Romania (Ro), and Czech Republic(Cz)……………………………………………………………………………………………………..
119
Figura 3.5.1.- Calibration curves obtained for sulfanilamide (A) and sulfamethoxazole (B) in methanol solution (white circles) and in standard additions in blank honey sample (asterisks)…………………………………….……………….
137
Figura 3.5.2.-Selected reaction monitoring (SRM) chromatogram of a blank
honey extract (A) and the same honey fortified at 20 g/kg (B) with all the sulfonamides studied.(1)Sulfanilamide; (2) Sulfathiazole; (3) Sulfadiazine; (4) Sulfapyridine; (5) Sulfamerazine; (6) Sulfamethazine; (7) Sulfamethizole; (8) Sulfachloropyridazine; (9) Sulfamethoxazole; (10) Sulfadimethoxine; (11) Sulfaquinoxaline; (M) Matrix…………………………………………………………………..………..
138
Figura 3.6.1.-Hazard Index of the mixture of 11 pesticides in the 22 samples (own-brands and well-known brands labelled as polyfloral). Different colours in the bars refer to the contribution of each pesticide (HQ) in the HI of each brand. Country origin: SE (Southern Europe); Ch (China); CA (Central America); SA (South America); Th (Thailand); Ce (Chile); ? (unknown origin)……………..…....
163
1. INTRODUCCIÓN
Introducción
1
1. Introducción
1.1.-La miel como producto
1.1.1. Formación de la miel
La miel, consumida por el hombre desde hace miles de años, es el producto de la unión
entre el mundo animal, la abeja (Apis melífera), y el vegetal, el néctar de las flores y/o
secreciones azucaradas de las plantas o insectos. Hoy en día sigue siendo el mismo
alimento que nuestros antepasados consumían con importantes propiedades
nutricionales y terapéuticas, así como con características sensoriales muy atractivas.
No se sabe a ciencia cierta desde cuando el hombre introdujo la miel en su dieta, sin
embargo, sí que hay constancia de que ya la recolectaba hace más de 8000 años, como
queda patente en las pinturas rupestres de la cueva de la Araña (Bicorp, Valencia). Hasta
la aparición del azúcar cristalizado, hace unos 2600 años, la miel ha sido el alimento para
endulzar por excelencia.
Las abejas producen la miel a partir de néctar o de secreciones azucaradas. El néctar es
una disolución acuosa de azúcares (sacarosa, glucosa y fructosa) en diferentes
proporciones y de otras sustancias como sales minerales, ácidos orgánicos, aromas, etc.).
Es segregado por las plantas en los nectarios, situados habitualmente en la flor, aunque
también en las hojas o estípulas. Estos nectarios se encuentran en lugares estratégicos
con la finalidad de que los insectos “atrapen” los granos de polen en su cuerpo y
garanticen así la polinización. Este hecho ocasiona que el polen, en mayor o menor
cantidad, se encuentre presente en la miel. Además del néctar, las abejas recogen y
transforman las secreciones azucaradas o mielatos de ciertas plantas, así como de ciertos
insectos chupadores de savia, homópteros, que viven parásitos sobre algunas plantas y
succionan de ellas la savia elaborada.
Las abejas recolectan las soluciones azucaradas que tienen a su disposición cerca de la
colmena, si bien es cierto que tienen predilección por ciertas especies botánicas (Crane,
1975). En una primera etapa, las abejas pecoreadoras, absorben con su lengua el néctar
de las flores que visitan o los mielatos, los introducen en su buche y vuelven a la colmena
para regurgitarlos, posteriormente, los vuelven a absorber o los pasan a otras abejas,
Introducción
2
incorporando así enzimas adicionales (diastasa y glucoxidasa) y continuando con la
transformación iniciada en el buche de la abeja recolectora. En este proceso se incorpora
también la enzima invertasa, que ayuda a transformar el néctar o las secreciones de las
plantas en miel. Los líquidos azucarados depositados en el panal siguen deshidratándose
y experimentando transformaciones bioquímicas. Las abejas ventiladoras con el
movimiento de sus alas producen unas corrientes de aire, introduciendo del exterior aire
seco y eliminando del interior el húmedo. De esta manera, consiguen rebajar el
porcentaje de humedad hasta llegar a un 16-20%, grado de maduración que tiene la miel
cuando las obreras operculan, es decir sellan la celda con una fina capa de cera. Es en
este momento cuando la miel está lista para ser recogida del panal por los apicultores
(Del Baño Breis, 2000). Este nivel de concentración de agua, hace a la miel un producto
biológico bastante estable, impidiendo que los hongos y las levaduras encuentren un
medio favorable para su desarrollo. Sin embargo, la miel sigue transformándose, por
acción de las enzimas, una vez extraída de los panales y durante su almacenamiento
posterior (Figura 1.1).
Figura 1.1.- Abejas depositando miel en el panal
1.1.2. Composición de la miel y su implicación en la calidad
La miel es básicamente una solución de azúcares en agua y aproximadamente un 1% de
otros constituyentes menores (Tabla 1.1). Estos componentes le confieren unas
características de color, sabor y aroma típico, así como ciertas propiedades físico-
químicas, típicas del origen botánico de las plantas de las que procede. Las características
intrínsecas de cada miel, además, pueden variar en función de la climatología, el estado
de la colmena y las prácticas apícolas (Sáenz & Gómez, 2000).
Carbohidratos
Los carbohidratos son los principales componentes de la miel, siendo los mayoritarios los
monosacáridos fructosa y glucosa, representado solo ellos casi el 80%. En menor
proporción se encuentran los disacáridos sacarosa y maltosa, y por último otros
disacáridos y oligosacáridos como erlosa, turanosa, melecitosa, etc. La composición en
Introducción
3
azúcares de una miel puede estar relacionada con: la procedencia botánica, el estado de
madurez, la adulteración, la capacidad de cristalizar, la capacidad de fermentación, el
desarrollo de hidroximetilfurfural (HMF), etc.
Tabla 1.1- Rango de variabilidad de los componentes químicos de la miel
Componentes mayoritarios (99%) g/100g
Fructosa 21.7-53.9 Glucosa 20.4-44.4 Sacarosa 0.0-7.6 Otros azucares 0.1-16.0
Componentes minoritarios (1%) g/100g
Minerales 0.02-1.03 Nitrógeno (proteínas) 0.00-0.13 Enzimas >0.1% Aromas >0.1% Otros >0.1%
Fuente: Codex alimentarius, 2001
La relación de ciertos monosacáridos, como son la fructosa y glucosa (F/G), puede ser útil
para la caracterización botánica de las mieles. Así por ejemplo, altos ratios F/G son
propios de la mieles de acacia y castaño, por el contrario bajos ratios F/G son típicos de
mieles de girasol, colza y diente de león (Persano-Oddo & Piro, 2004).
El porcentaje de sacarosa de una miel puede depender no solo de su procedencia
botánica sino también de su estado de maduración. Una miel extraída del panal antes de
que esté madura, puede tener un excesivo contenido en sacarosa, pudiendo superar el
nivel permitido por la legislación (máximo de 5% en general, o entre 10%-15% en ciertas
excepciones) (Serra-Bonvehí & Ventura, 1995). El contenido del resto de azúcares
minoritarios (producidos por la acción de la invertasa) es mayor en las mieles de mielada
que en las de néctar, difiriendo poco entre ellas (Bogdanov, 2011).
El conocimiento de la composición de los diferentes azúcares presentes en la miel puede
proporcionar información sobre su posible adulteración. La miel puede adulterarse por
la adición directamente de jarabes azucarados (jarabes de azucares invertidos, jarabes de
caña de azúcar o remolacha, jarabes de arce, etc.).
La relación de los dos principales azucares de la miel, fructosa y glucosa (F/G), condiciona
tanto la capacidad de cristalizar como su velocidad. La miel es una solución sobresaturada
de azúcares; por ello en su estado líquido es bastante inestable y tiene una tendencia
natural a cristalizar. La fructosa al ser más soluble en agua permanece fluida, siendo la
glucosa la que cristaliza debido a su menor solubilidad (Hamdan, 2010). Una miel con un
ratio F/G >1.33 tardará en cristalizar, sin embargo, cuando éste sea <1.11 el fenómeno
Introducción
4
se producirá más rápidamente (Smanalieva & Senge, 2009). White et al. (1962)
demostraron que la concentración de glucosa y el contenido de humedad de una miel
(G/H) ayudan a predecir la tendencia de una miel a la cristalización. Así, para valores de
esta relación inferiores a 1.60 la capacidad de cristalización de una miel es prácticamente
nula o muy lenta, en cambio para valores superiores a 2 es más rápida y completa.
Además de estos factores, el fenómeno de la cristalización también estará condicionado
por la presencia en la miel de “semillas o núcleos de cristalización”, como son: granos de
polen, partículas de cera, impureza, burbujas de aire, etc.
Un problema asociado a la cristalización es la fermentación, algunas mieles cristalizan
uniformemente y otras parcialmente, formando en muchas ocasiones 2 fases; quedando
los cristales en el fondo y la fase más liquida en la superficie. Cuando esto sucede, en la
parte superior rica en agua se favorece el desarrollo de las levaduras (propias de un
alimento de origen animal, principalmente genero Bacillus), comenzando entonces la
transformación de los azúcares en alcohol y la producción de dióxido de carbono (Zamora
& Chirife, 2006). Es necesario un contenido de 100 levaduras/g miel para que el proceso
tenga lugar, así como un contenido en humedad superior a 20% y una temperatura
ambiente de unos 25ºC (Díaz-Moreno, 2009).
La deshidratación espontanea en un medio ácido como es la miel, especialmente de la
fructosa y de la glucosa, produce un aldehído cíclico llamado hidroximetilfurfural (HMF)
(C6H6O3) (Figura 1.2). Esta reacción se acelera por el calor y el tiempo. La miel recién
extraída con buenas prácticas de manipulación contiene pequeños porcentajes de este
aldehído (0 a 7 mg/kg), sin embargo, se incrementa con el sobrecalentamiento y/o
envejecimiento de la miel, siendo más pronunciado cuanto más ácida es esta (Sancho et
al., 1992). Por este motivo, para garantizar que la miel llegue al consumidor lo más fresca
posible y sin alteración de sus características intrínsecas, la legislación establece que este
parámetro no debe ser superior a 40 mg/kg durante toda la vida útil de la miel. La
destinada a uso industrial y miel de origen procedente de regiones de clima tropical y
mezclas de estas puede llegar a contener un máximo de 80 mg/kg (Tabla 1.3) (Real
Decreto 1049/2003). Es por ello que en las transacciones comerciales, este parámetro se
utiliza como indicador de la frescura de la miel (Fallico et al., 2004).
Introducción
5
Figura 1.2.- Reacciones de equilibrio entre la fructosa y el HMF en medio ácido.1: Fructosa, 2: Fructofuranosa, 3–4: Etapas intermedias de deshidratación, 5HMF (Stahlberg et al., 2011).
Humedad
El nivel de agua de una miel contribuye en su peso específico, viscosidad, sabor, calidad
y en definitiva en el valor comercial del producto (Sáenz & Gómez, 2000). Su contenido
está comprendido entre 14 y 21%, con un promedio de 17%. El contenido de agua va a
depender del origen botánico de la miel, las condiciones climáticas, la temporada de
producción, la manipulación humana y las condiciones de almacenamiento, y por todo
ello, va a influir en su calidad final (Gallina et al., 2010). Bajos porcentajes de agua
dificultan la manejabilidad de la miel, por el contrario altos porcentajes pueden propiciar
efectos negativos ya mencionados como es la fermentación. Si una miel llega a la
industria con un alto contenido en agua, propiciado bien por la climatología anual o por
una extracción prematura del panal, existe el riesgo de que posteriormente en el
almacenamiento pueda fermentar por levaduras osmófilas. Aunque esto no ocasiona
ningún peligro de salud para el consumidor, invalidará el producto para su
comercialización, con la consecuente pérdida económica para la industria (tiempo y
dinero). Con valores de humedad <17.1% desaparece prácticamente este peligro. Sin
embargo, con valores por encima de este nivel la posibilidad de fermentación aumenta
considerablemente en función de la carga microbiana (Belitz & Grosch, 1997).
Enzimas
Las enzimas son componentes minoritarios de la miel, cuya presencia diferencia
claramente la miel de otros edulcorantes. Algunas de ellas son introducidas por las abejas
procedentes de su tracto gastrointestinal y otras proceden del néctar o mielatos (Anclan,
1998). Las enzimas son las encargadas de transformar los azúcares del néctar o de los
mielatos. Las diastasas (α-amilasas), son enzimas transformadoras de almidón, que
proceden tanto de la abeja como del néctar. No está clara su función en el proceso de
obtención de la miel, ya que el néctar no contiene almidón (White, 1978; Ropa, 2010),
aunque al parecer estas enzimas participa en la digestión del polen (del Baño-Breis,
Introducción
6
2000). La invertasa (α-glucosadas), de origen animal, es la encargada de desdoblarla
sacarosa en fructosa y glucosa (E 1).
C12H22O11 +H2O ------ C6H12O6 +C6H12O6 Eq. (1)
Las mieles frescas no procesadas de diferentes procedencias florales presentan grandes
variaciones en su actividad diastásica (Tabla 1.2). Esta variación es debida a las diferencias
de pH entre mieles, a la cantidad de néctar que procesan las abejas en un momento dado
y a las diferencias en la forma de recolección de cada abeja pecoreadora (Nacional Hoyen
Borde, 2015). Aunque de manera general las enzimas son sensibles al calor, la diastasa es
relativamente estable al calor y al almacenamiento, por el contrario la invertasa es mucho
más susceptible. Estas dos enzimas juegan un papel muy importante en la calidad de la
miel y son usadas como indicadoras de la pérdida de su frescura (Bogdanov, 2011), tanto
por envejecimiento como por un excesivo calentamiento durante su procesado. Cabe
destacar que la legislación solo contempla la actividad de la diastasa, aun siendo la más
estable al calor. Esto unido al hecho de la gran variabilidad que presenta esta enzima en
los diferentes tipos de miel, hace pensar que su nivel no está muy correlacionado con la
calidad de la miel (Visquert, 2015). Aun así, la legislación, para preservar la naturaleza
biológica de la miel, establece que el contenido enzimático de diastasas no deberá ser
menor de 8 ID (escala Schade), salvo en el caso de mieles que por su origen de manera
natural tienen bajo contenido enzimático (p.e. miel de cítricos), en cuyo caso este valor
no debe ser menor de 3 ID.
La glucosa-oxidasa, de origen animal, actúa sobre la glucosa para producir ácido glucónico
y peróxido de hidrogeno. Este compuesto actúa como antibacteriano protegiendo a la
miel hasta que esté madura (Eq 2). Es una enzima sensible a la luz, que está activa en el
néctar, pero inactiva en la miel, aunque puede volver a activarse si la miel es diluida. Otras
enzimas como la fosfatasa acida y la catalasa se encuentran también presentes en las
mieles (White, 1978; Crane, 1990; Sáenz & Gómez, 2000).
D-glucosa + O2-----D-gluconolactona----- ácido glucónico+H2O2 Eq. (2)
La fosfatasa ácida, está presente principalmente en el polen, aunque también en el
néctar. Esta enzima está relacionada con la fermentación de la miel, de manera que
aquellas presenten mayores valores de actividad de esta enzima tendrán una mayor
tendencia a fermentar. Dicha actividad está fuertemente influenciada por el pH de la
miel, por lo que a mayores valores de pH, mayor es su actividad.
El estudio de la actividad enzimática de una miel puede ayudar a detectar cierto tipo de
adulteraciones. En ocasiones se adiciona fraudulentamente a la miel jarabes de azúcares
invertidos (HFCS) (producidos enzimáticamente a partir de -amilasay-amilasa), o bien
Introducción
7
se adicionan directamente enzimas (-fructofuranosidasa) para compensar la
disminución de actividad enzimática por la adición de estos jarabes.
Tabla 1.2.- Contenido diastásico en mieles de diferentes orígenes. Unidades expresadas como valores de la escala Schade.
Origen floral Índice diastasico (ID)
Azahar 4.25 Trébol 5.73 Pimienta 13.2 Milflores 22.0 Eucalipto 24.0 Trigo sarraceno 36.8
Un método que posibilita la detección de algunos de estos enzimas ajenos a la miel, se
basa en la comparación de la actividad enzimática (diastasa) antes y después de someter
la miel a un tratamiento térmico; éste destruye completamente la diastasa y no a las
enzimas adicionadas. Si después del tratamiento se detecta actividad enzimática, ésta es
debida a la presencia de las enzimas resistentes adicionadas en la adulteración. Para
poner de manifiesto la adulteración de la miel por adición de siropes de azúcar invertido
tipo C3, se evalúa la presencia de la enzima -fructofuranosidasa, mediante la detección
cromatográfica de los metabolitos que ésta produce a partir de la adición de un sustrato
específico.
Compuestos aromáticos
Los compuestos volátiles (ácidos alifáticos y aromáticos, aldehídos, cetonas y alcoholes,
etc.), junto con los azúcares, los ácidos de la miel, así como ciertos componentes del
néctar contribuyen definitivamente en su aroma y sabor. Muchos estudios se han llevado
a cabo en las últimas décadas en relación a los componentes volátiles de las diferentes
variedades de mieles y distintas procedencias (Radovic et al., 2001; Soria et al., 2003; De
la Fuente, 2005; Castro-Vázquez et al., 2009). En algunos de ellos la caracterización de la
fracción volátil ha ayudado en la autentificación de su origen botánico (Kadar et al, 2011).
Un ejemplo claro de este hecho lo encontramos en la miel de azahar (Citrus spp.), que se
caracteriza por la presencia en ella del metilantranilato (MA). Se trata de un compuesto
volátil específico del néctar de la miel de cítricos, y característico de aroma propio de su
flor (ISO 5496,2006). Su presencia en la miel indica que las abejas han recogido el néctar
de las flores de cítricos (White & Bryant, 1996; Castro-Vázquez et al., 2007). Por esta
razón el MA ha sido y sigue siendo usado como marcador de este tipo de mieles y puede
ser una buena herramienta para la clasificación de la miel de azahar cuando el nivel de
contenido en polen es muy bajo, como suele suceder en este tipo de mieles (Sesta et al.,
2008).
Introducción
8
Antioxidantes en la miel
La miel fresca posee una actividad antioxidante significativa, similar a la de muchas frutas
y verduras (Gheldof et al., 2002). Las plantas contienen una variedad de derivados
polifenólicos, y por lo tanto, cuando las abejas recogen el néctar o los mielatos, estos
compuestos bioactivos son transferidos de las plantas a la miel (Silici et al., 2010). Los
compuestos fenólicos, antioxidantes naturales, (especialmente flavonoides) constituyen
un grupo importante por sus propiedades funcionales y su importancia terapéutica (Yao
et al., 2004). Mieles con altos niveles de antioxidantes, pueden contribuir a mejorar la
salud humana (Oroian & Escriche, 2015).
Varios estudios han demostrado una fuerte correlación entre el contenido de
compuestos fenólicos en mieles de diversas fuentes florales, sus capacidades
antioxidantes y actividades antibacterianas (Gheldof et al., 2002; Meda et al., 2005;
Escriche et al., 2011; Álvarez -Suárez et al., 2012; Escuredo et al., 2012).
Otros componentes
Además de los anteriormente citados, la miel contiene otros componentes entre los que
destacan: aminoácidos, ácidos, proteínas y minerales. Los aminoácidos libres están
presentes en aproximadamente 100 mg/100 g materia seca. Entre éstos destaca la
prolina (procedente de las abejas) que constituye entre el 50 y 85% de esta fracción
aminoacídica. El ácido más abundante es el ácido glucónico, aunque también están
presentes otros como: acético, butírico, cítrico, fórmico, láctico, málico, piroglutámico, y
succínico. La presencia de estos ácidos le confiere el pH ácido a la miel, comprendido en
un rango de 3.4 a 6.1. Este medio ácido inhibe la presencia y crecimiento de
microorganismos. Las proteínas en la miel representan una cantidad muy pequeña y su
presencia está ligada, mayoritariamente a los granos de polen que se encuentran en ella;
su contenido aproximado es de 0.26 g/100 g de proteínas. El contenido en sales minerales
varía notablemente con relación al origen botánico, a las condiciones edáfico-climáticas,
así como a las técnicas de extracción; oscila entre 0.1 y 0.2 %, siendo el elemento
dominante el potasio, seguido de: cloro, azufre, sodio, calcio, fósforo, magnesio,
manganeso, silicio, hierro y cobre (Belitz & Grosch, 1997). Algunos autores han observado
correlación entre el contenido en minerales y el color de las mieles (González-Miret,
2005).
Introducción
9
1.2.-Marco legislativo
Los criterios de calidad para la miel están definidos tanto por el Codex Alimentarius (2001)
para la miel como por la Comisión Europea (Directiva 2001/110/CE), con escasas
diferencias entre ellos. El Codex es un referente internacional que ha servido de base
para elaborar normas más específicas a nivel de cada país (Oyarzun et al., 2005). En
ambos estándares se describen los parámetros de calidad de la miel, definiendo sus
mínimos y máximos. En España, el Real Decreto 1049/2003, transposición de la citada
Directiva, establece la norma relativa a la calidad de la miel y define los criterios de
diferentes parámetros físico-químicos (comentados en el apartado 1.1.2) (Tabla 1.3), así
como otras condiciones que deben de cumplir las mieles vendidas en el territorio
español. Se recoge que la miel vendida como tal no debe tener ningún ingrediente
(incluyendo aditivos alimentarios), no debe contener nada que no sea miel. Asimismo, no
debe presentar ningún aroma o sabor que provenga de su procesado o almacenamiento.
Tampoco debe presentar principios de fermentación o estar fermentada. La miel no debe
ser sometida a calentamiento hasta tal punto que éste le pueda inducir cambios
esenciales en su composición o daños en su calidad.
Los métodos analíticos para el análisis de los parámetros fisicoquímicos, son recogidos
por la legislación española en el BOE 145 (1986). Además, también se han publicado
métodos revisados y recopilados por la Comisión Internacional de la Miel (IHC) (Bogdanov
et al., 2002). La IHC se constituyó en el año 1990 con la finalidad de desarrollar y mejorar
las normas relativas a los estándares de calidad de la miel.
Los criterios de calidad recogidos en las diversas normativas se centran en tres conceptos:
autenticidad, frescura y seguridad.
Introducción
10
Tabla 1.3.- Características composicionales de la miel según los estándares europeos (Directiva 2001/110/CE).
Contenido de azúcares
-Contenido de fructosa y glucosa (suma de ambas)
Miel de flores…………………………………………………………………………………….……. ≥60 g/100 g
Miel de mielada, mezclas de miel de mielada con miel de flores….……….… ≥ 45 g/100 g
-Contenido de sacarosa
En general……………………………………………………………………………………….….…. ≤5 g/100 g
Falsa acacia (Robinia pseudoacacia), alfalfa (Medicago sativa), Banksia de
Menzies (Banksia menziesii), Sulla (Hedysarum), Eucalipto rojo
(Eucalyptus camaldulensis), Eucryphia lucida, Eucryphia milliganii, Citrus
spp. …………………………………………………………………..……………………….…………..
≤10 g/100 g
Espliego «Lavandula spp.», borraja «Borago officinalis»…………..….…………
≤15 g/100 g
Contenido de agua
En general……………………………………………………………………………….…………………….. ≤20%
Miel de brezo «Calluna» y miel para uso industrial en general………....….… ≤23%
Miel de brezo «Calluna vulgaris » para uso industrial……………………….……….
≤25%
Contenido de sólidos insolubles en agua
En general………………………………………………………………………………………………….….. ≤0,1 g/100 g
Miel prensada………………………………………………………………………………….…………....
≤0,5 g/100 g
Conductividad eléctrica
Miel no incluida en la enumeración de los dos párrafos más abajo indicados, y
mezclas de estas mieles……………………………………………………………….……….……....
<0,8 mS/cm
Miel de mielada y miel de castaño, y mezclas de éstas, excepto con las mieles
que se enumeran a continuación…………………………………….…..…………………………
≥0,8 mS/cm
Excepciones: madroño «Arbutus unedo», argaña «Erica», eucalipto, tilo «Tilia
spp», brezo «Calluna vulgaris», manuka o jelly bush «Leptospermum», árbol
del té «Melaleuca spp.»
Ácidos libres
En general………………………………………………………………..……………….…………….…..… ≤50 miliequivalentes
de ácidos/ kg
Miel para uso industrial………………………………………..…………………………………….…. ≤80 miliequivalentes
de ácidos/ kg
Índice diastásico (escala de Schade)
En general, excepto miel para uso industrial…………………..…………….…….……….. no menos de 8
Mieles con un bajo contenido natural de enzimas (por ejemplo, mieles de
cítricos) y un contenido de HMF no superior a 15 mg/kg………………………….…..
no menos de 3
HMF
En general, excepto miel para uso industrial…………………………….………….………… ≤40 mg/kg
Miel de origen declarado procedente de regiones de clima tropical y mezclas
de estas mieles……………………………………………………………………………………..……….
≤80 mg/kg.
Introducción
11
1.2.1. Autenticidad y frescura
La miel al ser un edulcorante con precio elevado ha sido y es hoy día muy susceptible de
adulteraciones y fraudes. La miel adulterada apareció en el mercado mundial en la
década de 1970, coincidiendo con el desarrollo industrial de los jarabes provenientes de
materias primas vegetales como los de maíz de alta fructosa (JMAF) (Cordella et al.,
2002). Datos recientes publicados por la Comisión Europea demuestran que la miel sigue
siendo objeto de estos fraudes (Parlamento Europeo, 2013). Según este estudio, la miel
ocupa la sexta posición entre los diez alimentos con mayor riesgo de sufrir fraude
alimentario (aceite de oliva, pescado, alimentos ecológicos, leche, cereales, miel y jarabe
de arce, café y té, especias, vino y zumos de frutas). En la última década la miel
adulterada, procedente mayoritariamente de países terceros como China, están
causando un importante problema para el sector, por la competencia desleal que esto
implica (White, 2000; Bogdanov & Martin, 2002; Chen et al., 2011). La Tabla 1.4 muestra
como ejemplo algunas de las alertas alimentarias que al respecto han tenido lugar en los
últimos años.
Tabla 1.4.- Alertas relativas a mieles adulteradas de venta en comercios de distintos países.
Origen Fecha publicación
Malasia 2 octubre 2012
Nueva Zelanda 12 junio 2012
Nueva Zelanda 21 mayo 2012
Arabia Saudí 6 febrero 2013
Turquía 30 marzo 2013
Turquía 3 octubre 2011
Vietnam
12 septiembre 2012
Fuente: FDA (USA)
La extracción prematura de la miel del panal también implica un fraude ya que la
legislación define a la miel como “la sustancia natural….., que las abejas recolectan,
transforman combinándolas con sustancias específicas propias, depositan, deshidratan,
almacenan y dejan en colmenas para que madure” (Directiva 2001/100/CE; Real Decreto
1049/2003). La miel debe de madurar dentro del panal, y solo debe ser extraída por el
apicultor en el momento adecuado de maduración. Si se recolecta antes de tiempo, la
sustancia azucarada resultante tiene un elevado contenido en agua, llegando fácilmente
a sobrepasar el 25%. Este tipo de fraude, se ha observado también en mieles procedentes
de China y probablemente en otros países (Bogdanov & Martin, 2002).
El ultrafiltrado de la miel, tiene por objeto quitar su polen para impedir identificar la
fuente botánica y/o geográfica de la misma. Esta práctica dificúltala detección de posibles
fraudes, bien sea en la declaración del país o países de origen o bien en la variedad
Introducción
12
botánica expresada en la etiqueta; impidiendo por consiguiente su trazabilidad. Es un
procedimiento que requiere de alta tecnología, en el que la miel se calienta generalmente
diluida con agua, y se fuerza a pasar, a alta presión, a través de unos filtros de malla muy
pequeña, finalmente el exceso de humedad es eliminada. En el mercado de Estados
Unidos durante años se han encontrado mieles filtradas (Schneider, 2011).
Por ello, para proteger a la miel recientemente la unión europea modificó una parte de
la Directiva 2001/110/CE, estableciendo en la nueva Directiva 2014/63/UE que la
eliminación parcial o total del polen de la miel está específicamente prohibida y aduce
que “el polen entra en la colmena como resultado de la actividad de las abejas y está
presente en la miel de forma natural, con independencia de que los explotadores de
empresas alimentarias extraigan o no esa miel”.
Métodos analíticos para la detección de adulteraciones en miel
La Comisión Europea 2001/110/EC, con la finalidad de asegurar un negocio justo,
proteger a los consumidores del fraude y mantener un alto nivel de capacidad de su
identificación, está fomentando la investigación y el desarrollo de métodos analíticos que
faciliten la detección de dichas prácticas. Una parte importante del trabajo de la Comisión
Internacional de la Miel (IHC) en los últimos años se ha centrado en cuestiones relativas
a la autenticidad de la miel.
Son muchos los trabajos publicados por diferentes autores en relación a la aplicación de
técnicas analíticas que permiten detectar las adulteraciones de la miel; sin embargo,
ninguna de dichas técnicas resulta concluyente por si misma ya que, tal y como se ha
comentado, son muy diferentes los procedimientos llevados a cabo para realizar tales
adulteraciones: incorporación de jarabes (jarabes de maíz, de caña de azúcar, de agave,
etc.), adición de enzimas, incorporación de colorantes, extracción prematura de la miel
del panal, etc. Además, como sucede siempre, “la técnica de la adulteración” va siempre
un paso por delante de la de la “técnica de la detección”.
Entre las diferentes técnicas que actualmente existen para la detección de adulteraciones
en la miel cabe mencionar:
Estudio del residuo microscópico. Esta técnica se emplea como un primer
cribado para identificar la adición de jarabes de caña de azúcar o de maíz,
evaluando en la miel el residuo microscópico presente (Kerkvliet & Meijer, 2000)
y posteriormente verificando con la medida de su relación isotópica 13C/12C de
miel y su proteína (Simsek et al., 2012; Tosun, 2013).
Métodos isotópicos a través de la resonancia magnética nuclear (SNIF-NMR) que
permiten obtener los espectros característicos de la miel sin adulterar (Cote et
al., 2003). Posteriormente, se comparan éstos con las mieles sospechosas de
estar adulteradas.
Introducción
13
Evaluación del perfil de azúcares por métodos cromatográficos. Esta técnica es
especialmente recomendada para la detección de jarabes de remolacha (Elflein
& Raezke, 2005; Cabañero et al., 2006).
Espectroscopía de infrarrojos, por transformada de Fourier, que combinada con
análisis multivariantes son capaces de determinar el nivel de adición de azúcar
a la miel (fructosa, sacarosa y jarabe de maíz) (Sivakesava & Irudayaraj, 2001;
Kelly et al, 2006).
Cromatografía capilar de gases (GC) para la detección de oligosacáridos ajenos
a la miel. Especialmente útil para adición de jarabes de maíz ricos en fructosa
(HFCS) (Low & South, 1995).
Determinación de glicerol, butanediol y etanol mediante test enzimáticos que
permitirá determinar adulteración por “miel inmadura”. Se admite un máximo
de 300 mg/kg de glicerol para mieles de néctar y un máximo de 150 mg/kg de
etanol para mieles de néctar españolas e italianas Además estas técnicas se
complementan mediante la detección de altos niveles de levaduras muertas por
microscopía óptica. (Huidobro et al., 1994; Flores et al., 2002).
1.2.2. Seguridad
En materia de seguridad la miel, al igual que cualquier otro alimento, no debe contener
microorganismos o residuos químicos que puedan alterar la salud del consumidor.
Debido a la baja aw de la miel, desde el punto de vista microbiológico es muy estable y no
contiene microrganismos tóxicos. La única excepción puede ser la presencia de esporas
de Clostridium botulinum, motivo por el cual las empresas envasadoras suelen indicar en
la etiqueta que el producto no es apto para niños menores de 1 año.
Residuos químicos y su determinación
En lo que respecta a la presencia de residuos, la miel puede tener sustancias químicas
procedentes tanto de los tratamientos veterinarios (contaminación directa) como de la
contaminación agrícola (contaminación indirecta). Las abejas padecen enfermedades
infecciosas, y en ciertas ocasiones los apicultores se ven obligados a aplicar
medicamentos veterinarios. Las que más problemas causan a la apicultura a nivel
mundial, por afectar a larvas y pupas, son la Loque americana (AFB) producida por el
bacilo Paenibacillus larvae y la Loque europea (EFB) producida por la bacteria no
esporulante Melissococcus pluton (Hammel et al., 2008). Para el tratamiento de AFB se
utilizan compuestos de la familia de las sulfamidas y para la EFB se usan las tetraciclinas,
ya que las sulfamidas no tienen acción curativa para esta última (Mundo Apícola,
2012).Actualmente en la Unión Europea no se han fijado límite máximos de residuos
(LMR) para medicamentos veterinarios en los productos de la colmena, lo que significa
que éstos compuestos nunca deben estar presentes por encima del límite de
cuantificación del método analítico utilizado (Maudens et al., 2004). A pesar de su
Introducción
14
prohibición, no es infrecuente la utilización de sulfonamidas y otros antibióticos con fines
sanitarios apícolas.
Por otro lado, la abeja melífera se ve afectada por la presencia de parásitos que
disminuyen su producción, y en mayor medida provocan su muerte. El más importante
es el ácaro Varroa destructor (Anderson & Trueman, 2000), considerado como la más
problemática plaga de Apis mellifera en el mundo, la varroasis (Sammataro & Finley,
2004). Este ácaro provoca pérdidas directas de producción e indirectas en la polinización
de cultivos (Ledoux et al., 2000). El ácaro parasita las larvas de abeja en la propia celdilla
y, desde ese momento, la abeja llevará el parásito que, además de alimentarse de su
hemolinfa, le producirá un tremendo desgaste energético durante el pecoreo, llevándola
hasta la extenuación y por consiguiente a su muerte (Willians, 2000). La varroasis se ha
expandido con el tiempo y hoy se encuentra presente en todas las regiones de
importancia apícola. Para luchar contra este parásito se utilizan diferentes pesticidas con
acción acaricida. Entre los principios activos más usados actualmente en España
encontramos el coumafos (organofosforado), el amitraz (amidina) y el tau-fluvalinato
(piretroide). Los organofosforados y piretroides inhiben la transmisión de estímulos en el
sistema nervioso de los ácaros (Simon-Delso, 2015; Soderlund, 2012). Las amidinas son
antagonistas de los receptores de la octopamina en el cerebro de los parásitos,
provocando hiperexcitabilidad, seguidamente parálisis y muerte. Al aplicar estos
tratamientos en la colmena, si no se llevan a cabo de manera adecuada, pueden aparecer
posteriormente sus residuos en la miel.
La vía indirecta que ocasiona la presencia de residuos en la miel es la debida a los
tratamientos agrícolas llevados a cabo porros agricultores para proteger a los cultivos de
plagas y enfermedades (Kujawski & Namiesnik, 2008).Las abejas entran en contacto con
estos plaguicidas agrícolas durante la actividad de recogida del néctar (en un radio de 3-
6 km de la colmena) (Beekman & Ratnieks, 2000; Rial-Otero et al., 2007). Alrededor del
80% de los insecticidas agrícolas que se usan actualmente en Europa son
organofosforados y carbamatos (Blasco et al., 2011). En los últimos años han aparecido
otros insecticidas llamados neocotinoides, que vienen siendo muy usados, por presentar
una serie de ventajas en la agricultura: son altamente eficaces, selectivos, de efecto
prolongado y no presentan resistencia cruzada como los carbamatos, organofosforados
o piretroides sintéticos. Sin embargo cuando la abeja entra en contacto con los
insecticidas neonicotinoides su sistema nervioso central se ve afectado, provocando su
desorientación y muerte por su incapacidad de volver a la colmena, ya que son agonistas
en los receptores postsinapticos del receptor de la acetilcolina nicotínica del sistema
central de los insectos (Taner & Czerwenka, 2011).
La combinación de la exposición a dosis subletales de pesticidas, y la presencia de la
varroasis, junto con otros problemas que pueda tener la colmena, a menudo producen
un efecto sinérgico sobre el comportamiento de las abejas provocando su muerte,
originando lo que se viene denominando como síndrome de despoblamiento o
Introducción
15
disminución de las abejas en la colmena (Colony Collapse Disorder, CCD) (Williamson &
Wright, 2013.; Van Engelsdorp et al., 2009). Los dos últimos años han sido dramáticos en
relación al CCD, ya que un elevado número de colmenas lo mostraron provocando una
disminución de su rendimiento entre el 30 y el 50%. Este hecho ha determinado que este
episodio, en muchos casos, haya sido el más grave desde el primer embate de la varroa
a finales de los años 80 (Calatayud & Simó, 2015).
Control de los residuos
Actualmente, a este respecto, se exige el cumplimiento del Plan de Control de Residuos
acordado por la Unión Europea y llevado a cabo por la Autoridad Europea de Seguridad
Alimentaria (EFSA). Con ello, se persiguen los siguientes objetivos: garantizar la
protección de la salud humana, controlar y vigilar las transacciones de alimentos de
acuerdo a la ley, y facilitar el comercio mundial de piensos y alimentos seguros y
saludables; todo ello teniendo en cuenta las normas y acuerdos internacionales. Cuando
los pesticidas son usados de la manera recomendada, no deberían implicar un riesgo para
el consumidor. Sin embargo, una mala aplicación de los mismos (dosis, procedimiento,
período, etc.) podría causar la contaminación del aire, agua, suelos además de las
especies animales (García-Chao et al., 2010) y de sus productos. En la UE, desde
septiembre de 2008, ha entrado en vigor el Reglamento que establece las normas
revisadas sobre los residuos de plaguicidas (Reglamento CE 396/2005). En la base de
datos de la página web de la Comisión Europea (EU Pesticides database, 2015) se
encuentran los límites máximos de residuos (LMR) relativos a todas las matrices
alimentarias y a todos los plaguicidas. Los LMR establecidos de los diferentes pesticidas
para la miel abarcan un rango de concentraciones que van desde 10 hasta 50 g/kg. Por
otro lado el Reglamento 37/2010 sobre tratamientos farmacológicos en animales
productores de alimentos, en relación a las abejas (tejido diana: la miel) fija los límites
máximos de 2 productos acaricidas: el amitraz (puede estar presente hasta un máximo
de 200 g/kg) y el coumafos (puede estar presente hasta un máximo de 100 g/kg).
Métodos analíticos de control de residuos químicos. Validación
Para evitar introducir estos residuos en la cadena alimentaria y reducir riesgos para la
salud humana, el control de su presencia en la miel es un requisito fundamental de
seguridad alimentaria (Bargańska et al., 2013). Entre las diferentes técnicas analíticas que
se utilizan con este propósito, destacan el test Elisa y el Charm como técnicas cualitativas
(screening), ambas ampliamente establecidas para el análisis de sulfamidas y antibióticos.
Asimismo, existen diversos métodos cuantitativos tanto para el análisis de antibióticos,
sulfamidas y pesticidas. Entre estos métodos cabe destacar la cromatografía líquida y de
gases, unidas a diferentes técnicas de detección, como son de ultravioleta (Viñas et al.,
2004; Carrasco-Pancorbo et al., 2008), de fluorescencia (Pang et al., 2005; Tsai et al.,
2010) o de espectrometría de masas (MS/MS) (Kaufman et al., 2002; Khong et al., 2005;
Pang et al., 2006). Actualmente la técnica de elección es LC-MS/MS junto con GC-MS/MS,
Introducción
16
ya que permiten la cuantificación de residuos (a niveles de concentraciones de g/kg)
con una alta sensibilidad y selectividad en matrices biológicas.
Los laboratorios analíticos que se dedican al análisis de residuos químicos tienen la
obligación de validar los métodos analíticos antes de su aplicación. En este sentido, hay
que realizar una serie de comprobaciones que aseguren que el método es
científicamente correcto en las condiciones en que va a ser aplicado. En el proceso de
validación se deben comprobar sus características técnicas en cuanto a selectividad y
especificidad, sensibilidad, linealidad, límite de detección, límite de cuantificación,
exactitud y precisión. El proceso para determinar la exactitud de los métodos se lleva a
cabo con materiales de referencia certificados, si es posible, o con muestras enriquecidas
artificialmente en el caso de no disponer de los materiales de referencia adecuados. La
precisión del método se calcula mediante el análisis repetido de una misma muestra.
Respecto al control de calidad externo, el laboratorio debe participar en ejercicios de
intercomparación y ensayos de aptitud que se organizan a nivel nacional e internacional
en el contexto de los métodos a validar considerando tanto el tipo de matriz como de
analito.
Con respecto a la validación del método, existen guías, proporcionadas por diferentes
organizaciones: la Directiva 2002/657/EC, la guía Sanco, la International Conference for
Harmonization (ICH, 1996), la guía Eurachem (2014). Todas ellas presentan ligeras
variaciones, sin embargo, tienen un objetivo común: establecer un sistema armonizado
de aseguramiento de la calidad para asegurar la exactitud y la comparabilidad de los
resultados analíticos.
La Directiva 2002/657/EC, relativa al funcionamiento de los métodos analíticos y la
interpretación de los resultados, establece una serie de requisitos para llevar a cabo la
validación de métodos analíticos tanto cualitativos como cuantitativos. En ella se definen
los criterios de funcionamiento y otros requisitos que son función de la técnica usada,
para la detección y cuantificación (LC-UV-Vis, LC-fluorimetría, ICP-MS, LC-MS/MS,…). Para
demostrar que el método analítico cumple los criterios de funcionamiento la citada
directiva establece los parámetros que hay que estudiar e indica que la validación puede
realizarse a través de un estudio interlaboratorios, o de acuerdo con métodos
alternativos en un solo laboratorio, o mediante la validación interna (Tabla 1.5).
El Comité sobre Residuos de Plaguicidas (CCPR, 2013) de la Comisión del Codex
Alimentarius (FAO y OMS) en relación al Programa conjunto sobre Residuos de
Plaguicidas para su control en los alimentos, considera que la guía SANCO/2007/3131
ofrece una cobertura en profundidad de la validación de métodos para residuos de
plaguicidas. El documento SANCO Validación de métodos y procedimientos del control de
la calidad para el análisis de residuos de plaguicidas en los alimentos y los piensos
contiene directrices sobre criterios de rendimiento, incluyendo límites de recuperación y
precisión. Abarca las exigencias y evaluaciones a aplicar a los datos obtenidos de los
Introducción
17
métodos de análisis. Además la guía, en respecto a la utilización de técnicas de
espectrometría de masas, especifica los requisitos necesarios, en relación a las
intensidades relativas de los iones obtenidos, para la identificación de compuestos y sus
tolerancias máximas.
Tabla 1.5.-Criterios de validación de métodos analíticos según la Directica 2002/657/EC.
Parámetros a evaluar independientes del método
Parámetros a evaluar dependientes del método
Especificidad La recuperación Veracidad Respetabilidad Robustez: cambios menores Reproducibilidad intralaboratorio Estabilidad Reproducibilidad
El límite de decisión (CCα) Capacidad de detección (CCβ) Curvas de calibración Robustez: cambios importantes
1.3.-Clasificación industrial de la miel
1.3.1. Métodos tradicionales en la clasificación de mieles
El origen botánico del néctar o de los mielatos empleados por las abejas para producir
miel será determinante en sus características y propiedades organolépticas. La miel de
flores, se puede clasificar en: a) miel unifloral o monofloral, cuando el néctar procede
principalmente de un especie botánica, de la que toma el nombre (miel de colza, miel de
romero, miel de azahar, miel de girasol, miel de cantueso, miel de castaño, etc.) y b) miel
multifloral, polifloral o milflores, que es aquella procedente del néctar de diversas
especies botánicas, sin que predomine ninguna de ellas. Por otro lado cuando la materia
prima no ha sido el néctar, la miel se puede denominar como “miel de mielada”. Estas
mieles contienen menor número de pólenes de plantas entomófilas y una cantidad mayor
de pólenes de plantas anemófilas, restos de hifas, esporas de hongos y algas verdes
indicadoras de mielada. Se suelen considerar como bioindicadores de mielada
determinadas clorofíceas (algas verdes) del género Pleurococcus y, en menor medida,
especies de los géneros Chlorococcus y Cystococcus y que normalmente aparecen
agrupas en estructuras “coloniales” denominadas cenobios (Garcia-Pérez, 2003).
Las mieles monoflorales son importantes en el mercado de la miel, por tener un mayor
valor añadido. Su producción depende tanto del apicultor, de la gestión que este hace a
través de la selección del sitio y de la recolección selectiva, como del industrial que mezcla
diferentes mieles para conformar lotes homogéneos. El aumento de los niveles de
Introducción
18
exigencia por parte del consumidor y la apreciación que este tiene por la mieles
monoflorales, están abriendo nuevos nichos de mercado, que presentan una
oportunidad para el sector de la miel. En España y otros países como Italia y Francia casi
la mitad de la miel comercializada tiene una denominación botánica.
Las diversas denominaciones botánicas deben ser verificables, a fin de proteger al
consumidor, y de preservar la genuinidad de las mismas. Tanto el estándar para la miel
Codex Alimentarius (Codex Alimentarius, 2001) como el europeo (Comisión Europea,
2002) permiten usar denominaciones específicas para las mieles producidas a partir de
fuentes botánicas concretas (miel de romero, miel de eucalipto, miel de girasol,…),
siempre que éstas procedan esencialmente del origen indicado y tengan las
correspondientes características fisicoquímicas, organolépticas y microscópicas
(Persano-Oddo & Bogdanov, 2004). Sin embargo, aunque estas normas especifican
algunos límites para la denominación de miel de flores y mieles de mielada, no establecen
ningún criterio legal para mieles monoflorales, por lo tanto no garantizan un control
eficaz de la monofloralidad para estas denominaciones. Algunos autores (Gómez-Pajuelo,
2004; Sáenz & Gómez, 2000) así como ciertos laboratorios europeos han establecido
límites para las mieles monoflorales, que pueden servir como control de calidad a nivel
nacional, pero presentan el inconveniente de que estos límites pueden variar entre
países, lo que implica una dificultad para el comercio internacional de este tipo de mieles.
Existen D.O. que especifican que para que una miel pueda ser considerada como
monofloral de una especie botánica, ésta debe tener un mínimo porcentaje de polen de
dicha variedad de miel. Por ejemplo, están reconocidas entre otras, con una calidad
diferenciada mediante D.O.P. las mieles españolas de Granada, La Alcarria, Tenerife y
Villuercas-Ibores; con I.G.P. las mieles de Galicia; con Marcas de Calidad de comunidades
autónomas la miel de azahar y la miel de romero con Marca C.V., etc. Cabe mencionar
que a menudo, la importación de ciertas mieles monoflorales ha sido rechazada debido
al incumplimiento de los límites locales (Persano-Oddo & Bogdanov, 2004).
No es fácil definir una miel como monofloral, ya que como se ha dicho no hay referencia
de mieles puras monoflorales, puesto que las abejas pecorean siempre en diferentes
especies botánicas incluso cuando hay una especie predominante. Ciertos tipos de mieles
pueden ser producidos en varios países con diferentes niveles de 'unifloralidad', a raíz de
la mayor o menor presencia de la planta correspondiente, y esto puede conducir a una
percepción ligeramente diferente, de un país a otro. El enfoque clásico para verificar la
denominación botánica de la miel tiene en cuenta tres metodologías complementarias,
el análisis sensorial, el físico-químico y el melisopalinológico. La decisión de clasificar una
miel como monofloral se debe basar en una interpretación global delos resultados
obtenidos por estas tres metodologías. Este enfoque clásico para la definición de mieles
monoflorales conlleva el uso de técnicas laboriosas que deben ser llevadas a cabo por
personal experto. Este hecho es especialmente evidente en el análisis del polen, ya que
los técnicos encargados de esta tarea deben tener amplia experiencia en el
Introducción
19
reconocimiento de la morfología y otros aspectos de los pólenes de las diferentes
especies botánicas. El técnico también debe ser conocedor de los aspectos sensoriales
característicos de las diferentes variedades de miel. A pesar de la problemática
relacionada con estas dos metodologías, en la actualidad la industria sigue utilizándolos
para la clasificación botánica de las mieles como parte del control rutinario realizado en
la etapa de recepción.
Análisis melisopalinológico
La miel contiene células de polen como consecuencia del arrastre por adhesión al cuerpo
de la abeja. Este hecho es precisamente el fundamento del análisis polínico, que asume
que una determinada cantidad del polen de cada especie, permanece en la miel, y por lo
tanto a partir de su estudio es posible conocer su origen floral. En definitiva, el polen
proporciona una buena huella digital de la procedencia botánica, así como del medio
ambiente donde esa miel ha sido cosechada. El estudio y reconocimiento microscópico
de la morfología de los granos de polen de las diferentes especies botánicas, y la
asignación de las frecuencias relativas de aparición de las mismas, con la finalidad de
asignar una variedad a la miel, es lo que se denomina melisopalinología. Por lo tanto, el
análisis de polen es útil tanto para determinar el origen botánico de las mieles como de
la zona geográfica de procedencia (Persano Oddo & Bogdanov, 2004).
No existe un método oficial para llevar a cabo este análisis. La International Commission
for Bee Botany en 1978 elaboró y publicó un método melisopalinológico (Louveaux et al.,
1978), y aunque posteriormente diversos autores propusieron otras metodologías, hoy
en día sigue siendo un método establecido en la mayoría de los laboratorios europeos
que realizan controles de calidad rutinarios a la miel. En 2004 Von Der Ohe et al.
implementaron y validaron esta metodología, la consistencia del método fue probada en
un ensayo interlaboratorio en el que participaron 9 países europeos.
El principal punto crítico del análisis melisopalinológico sigue siendo la correcta
identificación del polen y la interpretación posterior de los resultados, así como de la
discriminación de las especies melíferas y/o nectaríferas. Es importante destacar que la
presencia por ejemplo de un 20% de polen de una determinada especie presente en la
miel no indica, sin más, que la miel se ha formado con un 20% de néctar procedente de
dicha planta, sino que esta proporción puede ser mayor o menor. De manera general
para poder definir una miel como monofloral, el contenido de polen de la especie vegetal
dominante deberá ser ≥ 45% (polen predominante). Se establecen excepciones para
especies en las que el polen puede estar supra-representado (castaño, eucalipto o
myosotis, etc.) o infra-representado (azahar, romero, tilo) (Celis & Díez, 1995; Sáenz &
Gómez, 2000).
Aunque no está claramente determinada la relación entre la cantidad de néctar usada
para producir una miel y la proporción de pólenes en el sedimento polínico de ésta, se
Introducción
20
sabe que depende de una serie de factores que deben ser tenidos en cuenta, son los
llamados factores primarios, secundarios y terciarios.
El aporte primario es el procedente de la flor en la que pecorea la abeja. Los granos de
polen caen en el néctar de la propia flor, por las acciones mecánicas bien del aire o bien
de ciertos animales. La succión de néctar por parte de la abeja arrastra estos granos en
suspensión. Este fenómeno está influenciado directamente por distintos factores:
la arquitectura floral (flores abiertas, tubulares, cerradas, etc.). Por ejemplo, en el caso de lavandas y cítricos, el polen se deposita en polinias o en los estambres curvados hacia fuera, lo que conlleva a que el néctar contenga poco polen.
coincidencia de la secreción nectarífera con la dehiscencia de la antera.
localización cercana de los estambres a los nectarios.
tamaño y ornamentación de los granos: influyen en la cantidad susceptible de ser filtrada por el proventrículo en el buche melario.
distancia entre la fuente nectarífera y la colmena: a mayor distancia, más tiempo dura la filtración y más se reduce el contenido polínico en la materia prima.
El aporte secundario al sedimento de la miel, se debe a granos de polen presente en el
microambiente que hay en el interior de la colmena. Estos entran a través de la piquera
por corrientes de aire o son traídos en el cuerpo de la abeja al rozar con las anteras.
El aporte terciario, lo constituye el modo de extracción de la miel del panal. El uso de
instrumentos para desopercular los cuadros provoca la apertura de las celdillas con
polen, incorporándose éste a la miel. Este aporte es relevante, sobre todo en aquellos
casos en que la extracción se realiza por prensado de los panales o tipo de colmena,
debido a que las celdillas que contienen polen (si las hay) son también procesadas y su
contenido pasa a formar parte del producto final. A partir de extracciones por
centrifugación, el aporte polínico terciario puede ser también considerable (Del Baño-
Breis, 2000).
Análisis sensorial
La calidad sensorial de un alimento determina la aceptación del mismo por parte del
consumidor y su decisión de volver a comprarlo, por ello debe de ser muy tenida en
cuenta por productores y envasadores al establecer la diferencia entre sus productos
(Díaz-Moreno, 2009). El consumidor de miel define sus preferencias en base a la vista, el
olfato y la boca. En el caso concreto de la miel, la evaluación sensorial va a permitir definir
su origen botánico, así como identificar y detectar algunos defectos tales como:
fermentación, impurezas, olores y sabores extraños, etc.
Queda claro que la miel de una determinada procedencia botánica está definida por
ciertas características organolépticas. Para una correcta clasificación de la miel en base a
su origen botánico, el dictamen organoléptico es fundamental. No solo es una ayuda para
completar la información obtenida en el estudio polínico y fisicoquímico; sino que en
ocasiones resulta determinante para sacar conclusiones (ej. miel de lavanda). Así, por
Introducción
21
ejemplo: la miel de romero tiene un aroma poco intenso, con notas alcanforadas y tonos
florales aumentando la intensidad de los aromas en boca; la miel de eucalipto se define
por su aroma intenso y persistente a madera mojada, con gusto dulce y notas ligeramente
ácidas y la miel de espliego por su aroma muy intenso, siendo al gusto dulce con tonos
ácidos y ligeramente salado (Gómez-Pajuelo, 2004). En la Figura 1.3 se muestra la rueda
resultado de la estandarización de la terminología para definir sensorialmente a una miel
(odour and aroma wheel for honey) realizada por un grupo de trabajo de la IHC (Piana et
al., 2004).
Figura 1.3.-Rueda de olor y aroma de la miel (IHC, 2001)
Análisis físico-químico y color
Los métodos físico-químicos utilizados para el control rutinario de la calidad de la miel
han sido validados y armonizadas por la Comisión Internacional de la Miel (IHC) y pueden
ser utilizados dentro del alcance tanto del Codex Alimentarius Honey Standard (Codex
Alimentarius, 2001) como de la Directiva de la Unión Europea (European Commission,
2002).
La conductividad eléctrica es un parámetro de calidad de gran utilidad para la clasificación
de mieles (Persano-Oddo & Piro, 2004). El hecho de que la conductividad eléctrica se
Introducción
22
correlacione bien con el contenido mineral de la miel, permite distinguir perfectamente
la miel de mielada de la de néctar. La conductividad se determina en un conductímetro a
20°C, considerando el peso de la muestra sin humedad; expresando el resultado en
mS/cm (Accorti et al., 1987).
La actividad enzimática difiere entre tipos de mieles, pudiéndose utilizar para su
clasificación siempre y cuando se trate de mieles frescas y crudas ya que como se ha
comentado anteriormente, algunas enzimas como la diastasa desciende con el
calentamiento y el almacenamiento (Visquert, 2015). La medida de la actividad diastásica
se puede llevar a cabo por 2 procedimientos: 1. Definido por el BOE 145/1986, basado en
la determinación de la velocidad de hidrólisis del almidón en una disolución de miel y
medición del punto final de la reacción por espectrofotometría a una absorbancia de
660nm; 2. El método Phadebas descrito por la IHC, se basa en la medida de la
hidrolización de un sustrato estandarizado (pastillas) por la acción de la enzima diastasa.
En ambos casos ocurre que esta reacción produce una solución azulada, cuya intensidad
es proporcional a la cantidad de enzima presente. Este método presenta la ventaja de ser
más rápido y fiable que el anterior (variabilidad del almidón usado), ya que usa un
sustrato estandarizado. En ambos casos, la absorbancia de la solución es directamente
proporcional a la actividad diastásica de la muestra.
Los azúcares de una miel; se pueden determinar por diferentes métodos, siendo la
cromatografía liquida (HPLC) el más extensamente utilizado, en diferentes modalidades:
detección de índice de refracción (IR), detección de la dispersión de luz (ELSD) o iónico
(PAD). La cromatografía de gases con ionización de llama (FID) es también útil en el
análisis de azúcares. Presenta la ventaja frente a la cromatografía líquida de permitir
mayor sensibilidad en la detección de azúcares minoritarios. Por el contrario, su mayor
inconveniente es que se requiere una etapa previa de derivatización de la muestra, lo
que lo convierte en un método muy tedioso. Otro método ampliamente extendido, por
su rapidez y facilidad en el análisis de azúcares, son los kits enzimáticos, especialmente
en lo que se refiere a la determinación de fructosa, glucosa y sacarosa. Este
procedimiento aunque no es especialmente precisos, sirve como método de rutina en el
control de calidad de la miel.
El colores la propiedad física percibida de manera más inmediata por el consumidor. Es
un criterio muy útil para la clasificación de mieles monoflorales, variando desde blanco
agua, a través de tonos ámbar, hasta casi negro. Algunas mieles presentan tonalidades
típicas, como son el color amarillo brillante (miel de girasol), verdoso o rojizo (miel de
tomillo, brezo). Para medir el color el método más extensamente usado es el colorímetro
PFund, que se basa en la comparación óptica de la miel con una escala de color. También
se puede medir con técnicas que miden la reflectancia o transmitancia de la muestra,
para ello se usan equipos conocidos como espectrofotómetros o colorímetros
triestimulos (espacios cromáticos CIELAB). En el año 2004 apareció en el mercado un
espectrofotómetro (Hanna) específico para medir el color de la miel, cuyos resultados se
Introducción
23
expresan en la misma escala de color Pfund (mm), y por su gran versatilidad es hoy en día
ampliamente usado en los laboratorios de control de calidad de mieles.
1.3.2. Métodos alternativos en la clasificación de mieles
En las últimas décadas se vienen desarrollando métodos alternativos para la clasificación
de mieles, que aunque están dando resultados prometedores a nivel científico, en su
mayoría no están validados ni armonizados para ser empleados como análisis de rutina.
En general se trata de técnicas analíticas que generan una enorme cantidad de datos, por
lo que para dar resultados concluyentes, se requiere del apoyo de técnicas estadísticas
multivariantes. En la Tabla 1.6 se resume la utilidad de los métodos tradicionales, así
como de los nuevos que actualmente se están desarrollando y aplicando.
Entre los nuevos métodos cabría mencionar:
La espectroscopia de infrarrojos (IR). Se fundamenta en la absorción de la radiación IR por
parte de las moléculas en vibración. En principio, cada molécula presenta un espectro IR
característico (huella dactilar), debido a que la mayoría de las moléculas tienen algunas
vibraciones que, al activarse, provocan la absorción de una determinada longitud de onda
en la zona del espectro electromagnético correspondiente al infrarrojo. De esta forma,
analizando cuales son las longitudes de onda que absorbe un compuesto en la zona del
infrarrojo, podemos obtener información acerca de las moléculas que componen dicho
compuesto. Así, en la miel se determinan los espectros significativos en las regiones de
absorción de diferentes grupos funcionales de los componentes de la miel, por ejemplo
los C–O y C–OH de la glucosa y fructosa. De esta manera se estudia el ratio F/G
característico de los diferentes orígenes botánicos y se obtienen sus F/G tipificados, que
posteriormente se compararan con los de la miel a estudiar.
Compuestos fenólicos (flavonoides). Son metabolitos secundarios de los vegetales,
necesarios para su desarrollo y defensa frente a infecciones por microorganismos que las
atacan. La miel al proceder de las plantas (néctares y mieladas) contiene pequeñas partes
de estos compuestos. Desde que se descubrió la existencia de compuestos fenólicos en
la miel ha resultado ser un importante tema de estudio. Por una parte ha resultado de
gran interés para conocer mejor sus propiedades beneficiosas en el organismo humano,
y por otra permite explorar un campo en la caracterización geográfica/botánica de este
producto (Tomás-Barberán et al., 1994). En un estudio llevado a cabo por estos autores
sobre miel de La Alcarria concluyen que al analizar mieles de distinto origen geográfico,
las mieles tropicales carecen de ciertos compuestos característicos hallados en La
Alcarria; y por otra parte analizando mieles monoflorales del mismo origen demostraron
que el análisis discriminante clasificó el 85,7% de las mieles monoflorales correctamente.
Estos autores concluyeron que el análisis de flavonoides puede ser de gran utilidad en
estudios de origen y caracterización de mieles.
Introducción
24
En un reciente estudio llevado a cabo por Escriche et al. (2014) sobre diferentes mieles
españolas, deduce que el origen botánico afecta al perfil de flavonoides y compuestos
fenólicos lo suficientemente como para permitir la discriminación entre ellas. En la miel
de cítricos encontraron predominio de hesperetina, en la de romero de kaempferol,
crisina, pinocembrina, ácido cafeico y naringenina y en la de mielada, miricetina,
quercetina, galangina, y en particular el ácido p- cumárico.
Análisis de los componentes volátiles por GC-MS, como una medida indirecta de la
percepción aromática de las mieles, es uno de los métodos más prometedores para la
diferenciación de mieles (Bogdanov et al., 2004). Son numerosos los compuestos que han
sido identificados como característicos de algunas fuentes florales, lo que se refleja en
las recientes publicaciones mencionadas en el punto 1.1.2. Los componentes volátiles
contribuyen significativamente al flavour de la miel y está claro que varían con el origen
floral. Aunque cabe señalar que los resultados del análisis, sobre los compuestos del
aroma de la miel, dependen de las técnicas de aislamiento, así como de los métodos de
detección. Un análisis cuidadoso de los componentes de la fracción volátil en la miel
podría ser una herramienta útil para la caracterización de la fuente botánica (Anklam,
1998).
Análisis de aminoácidos. Las relaciones entre las concentraciones de diversos
aminoácidos pueden ser útiles para determinar el origen geográfico de una miel. De igual
manera, los perfiles de aminoácidos podrían dar una indicación de la fuente botánica de
muestras de miel. En un estudio llevado a cabo sobre la composición de aminoácidos,
analizados por GC-MS, en mieles de acacia, cítrico, castaño, rododendro, romero y tilo se
concluyó que ciertos aminoácidos como arginina, triptófano y cistina son característicos
de algunos tipos de mieles florales. Sin embargo, es el perfil general de todos ellos el que
puede permitir la diferenciación entre tipos de miel e incluso en función del origen
geográfico (Pirini et al., 1992).
Análisis de proteínas. El análisis de proteínas mediante electroforesis de gel de
poliacrilamidas junto con la aplicación de un análisis discriminante, permitió una cierta
clasificación de mieles gallegas (Rodríguez Otero et al., 1990). Sin embargo, estos autores
llegaron a la conclusión de que el análisis de los perfiles de aminoácidos parece ser más
adecuado, para la detección del origen botánico y geográfico, que el de la composición
de proteínas.
La determinación de minerales y oligoelementos en la miel podrían ser adecuados para la
detección de origen geográfico, debido al hecho de que éstos se ven afectados en gran
medida por la contaminación ambiental. La investigación de estos perfiles de elementos
traza en combinación con técnicas de evaluación de datos estadísticos podría ser un
enfoque prometedor (Sevlimli et al., 1992; Rodríguez -Otero et al., 1995).
Introducción
25
Tabla 1.6.- Métodos para la determinación del análisis botánico de mieles (Bogdanov et al., 2004)
Métodos y Parámetros Estado
Métodos clásicos
Determinación de parámetros fisicoquímicos rutinarios: conductividad eléctrica, azucares, ratio F/G, actividad enzimática, prolina, color, rotación óptica, pH, acidez
Junto con el análisis sensorial y el polínico la conductividad y el ratio F/G son muy útiles Parámetros rutinarios pueden ser determinados por espectroscopia IR
Otros métodos
Determinación de polifenoles por HPLC Presenta buenos resultados, aunque son métodos laboriosos para ser de rutina
Determinación de componentes volátiles por espacio de cabeza o SPME seguido de GC-MS o narices electrónicas
Métodos prometedores, que debería ser investigados y desarrollados para análisis cuantitativo de volátiles
Lenguas electrónicas Son también prometedoras, pero no se encuentran disponibles en laboratorios de alimentos de manera habitual
Aminoácidos Tienen algún poder discriminante pero dependen del origen geográfico
Análisis de inmunotransferencia de las proteínas de la miel, originadas por el polen
Método complementario al clásico melisopalinológico
Elementos traza Muestra alguna discriminación, pero puede depender del origen geográfico y de la climatología
Ácidos carboxílicos alifáticos Limitado poder discriminante ya que muchos ácidos son aporte de la abeja
Espectroscopia de infrarrojos (IR) Prometedor nuevo y rápido método, que debería ser desarrollado
Espectrometría de masas por pirolisis Prometedor nuevo método, aunque necesita cara instrumentación
La composición de ácidos orgánicos en la miel. El perfil de ácidos orgánicos y su evaluación
mediante métodos estadísticos puede resultar útil para proporcionar información
adicional sobre mieles de diversas fuentes. En miel de rewarewa de Nueva Zelanda
(Knightea excelsa), se han identificado treinta y dos ácidos dicarboxílicos alifáticos
(analizados por GC), tres de ellos se han propuesto como marcadores (Wilkins et al.,
Introducción
26
1995). Cherchi et al. (1994) en un estudio de ácidos orgánicos alifáticos de mieles italianas
describieron un método de extracción en fase solida (SPE) y posterior análisis por HPLC.
Las lenguas electrónicas. Entre las técnicas actualmente más prometedoras, destacan las
lenguas electrónicas, ya que se trata de equipos de medida rápidos y de bajo coste
(García-Breijo, 2005). Según la IUPAC, una lengua electrónica es "un sistema multisensor,
que poseen una baja selectividad y que utilizan procedimientos matemáticos avanzados
para el procesamiento de las señales obtenidas. Estos procedimientos matemáticos están
basados en la formación de Patrones de Reconocimiento y/o Análisis de datos
Multivariados, como son las Redes Neuronales Artificiales (RNA), los Análisis por
Componentes Principales (PCA), etc. (Campos et al., 2013). Las lenguas electrónicas están
siendo aplicadas en el control de calidad de diferentes alimentos: aguas minerales
(Martínez-Máñez et al., 2005), (Zhuiykov, 2012), vinos (Campos et al., 2013), leche (Dias
et al., 2009), miel (Dias et al., 2008; Kadar, 2011), etc.
La lengua electrónica consta de: un conjunto de sensores de distinta especificidad, una
instrumentación para adquirir la señal y un sistema de procesado de datos (ver Figura
1.4) (Jiménez et al., 2002). Los sensores electroquímicos transforman el efecto de la
interacción electroquímica analito-electrodo en una señal. El instrumento para adquirir
la señal (sistema de medida), se encarga de captar las señales eléctricas generadas por
los sensores, y, posteriormente, digitalizarlas y enviarlas al sistema de procesado de
datos. Sistema de procesado de datos (Software), creado con los algoritmos apropiados,
permite procesar las señales obtenidas por el sistema de medida (Jiménez et al., 2002).
Figura 1.4.- Esquema de los componentes que forman la lengua electrónica (Alcañiz, 2011).
Introducción
27
1.4.-El sector
1.4.1. Producción primaria
El proceso de producción de la miel comienza cuando el apicultor decide poner las
colmenas en una determinada zona geográfica, con la finalidad de cosechar un tipo de
miel determinado. Una vez la miel ha sido producida por las abeja y está ya madura el
apicultor extrae la miel de los panales, bien in situ en el campo o bien transportando los
cuadros para extraer la miel posteriormente. El paso siguiente es extraerla miel
(normalmente por centrifugación), eliminando la capa de cera que han depositado las
abejas para tapar las celdillas (desoperculado) (Figura 1.5). La miel se recoge en bidones
metálicos (aprox. 300 kg) que se cierran y serán la materia prima que entra en las
industrias de procesado y envasado de miel.
Figura 1.5.- Desoperculado de las celdillas y extracción de miel por centrifugación
La calidad con la que entra la miel en la industria es responsabilidad del apicultor. De él
depende cosechar en el momento adecuado de maduración (humedad) y conservar los
bidones en un lugar fresco y seco. Pero además, hay otro aspecto importante relacionado
con los tratamientos veterinarios. Las abejas desarrollan infecciones y se requiere la
aplicación de tratamientos químicos con antibióticos, sulfamidas, acaricidas, etc. Estos
tratamientos de deben aplicar en el momento adecuado y a dosis correctas. De no ser
así, el resultado es una miel contaminada por dichas sustancias.
En los últimos años los problemas de presencia de residuos han aumentado, por ello las
administraciones y los compradores están cada vez más alerta. Otra fuente de residuos
químicos en la miel es la debida a los pesticidas utilizados en los tratamientos agrícolas,
que llegan a la miel por la ubicación de los panales próximos a zonas de cultivo. Este
problema es usual en mieles de cultivo como el azahar, el girasol, el almendro, etc.
Introducción
28
1.4.2. Procesado de la miel y controles
En la industria actual el control de la calidad del proceso es fundamental para conseguir
un producto adecuado que cumpla los requisitos y exigencias establecidos, y esto tiene
especial relevancia en la industria alimentaria, ya que el producto final va a tener
incidencia en la salud humana. Por parte de las administraciones y de los consumidores
cada vez es mayor la exigencia de alimentos con mayores índices de calidad y de
seguridad. La administración debe asegurar un nivel elevado de protección de la salud de
las personas, y el consumidor tiene derecho a disponer de un producto adecuado a sus
exigencias y expectativas.
La industria de la miel, como parte del sector agroalimentario, debe controlar la calidad
tanto en la recepción de su materia prima, como en el proceso, hasta la expedición del
producto terminado. Entre los parámetros más frecuentemente controlados por las
empresas envasadoras los residuos químicos ocupan un lugar destacado, además de las
características organolépticas (sabor y aroma), color, contenido de humedad, frescura de
la miel (medida por la diastasa y el contenido de HMF), composición de azúcares
mayoritarios y el examen microscópico para la determinación de origen botánico y
geográfico. Este control de calidad de la miel tiene dos propósitos principales, por un lado
el de verificar su autenticidad, es decir, para revelar posibles fraudes tales como mieles
adulteradas etc. y por otro el determinar su calidad con respecto a las necesidades del
procesador y del mercado.
La mayoría de las empresas envasadoras de miel tienen establecidos ciertos parámetros
de calidad internos tanto para la materia prima que adquieren, bien directamente de los
apicultores, bien de terceros, así como para el producto terminado listo para la venta. De
esta manera controlan la producción, ajustando los costes a los requisitos del producto y
a sus diferentes niveles de calidad. Dichos niveles de calidad pueden ser los requeridos
por el mercado, o bien por otra empresa bajo cuyo sello se comercializará el producto. El
proceso que se lleva a cabo en la industria de manera general sigue el diagrama de flujo
de la Figura 1.6.
Introducción
29
Figura 1.6.- Diagrama de flujo del procesado de miel.
Retirada impurezas
Almacenamiento
Introducción de Lote a Cámara
≤53ºC/24-48 h
Creación de un Lote
Volcado de bidones en la balsa y
Homogeneización
Filtrado
Recepción y Etiquetado de bidones
Toma de muestra
Análisis
según ETs
Envasado en bidones
Almacenamiento
No OK OK
Expedición
Introducción
30
1.4.3. España en el contexto de producción y comercialización de miel
Según datos extraídos de la FAO, casi la mitad de la producción mundial de miel del año
2012 se concentró en Asia (737.000 toneladas), correspondiendo a China 436.000
toneladas, lo que representa el 60%. El segundo continente productor fue Europa seguido
de América y finalmente África y Oceanía (Figura1.7).
Figura 1.7.- Producción mundial de miel en 2012 (en Toneladas)
Dentro de Europa los principales productores de miel en 2012 fueron Ucrania, España y
Rumania (Figura 1.8). España cuenta con el mayor número de colmenas y tasa de
profesionalización, dentro de la Unión Europea. El censo total de colmenas, verificado
sobre la base del Registro de explotaciones apícolas en el estado español, ascendió a
2.533.270 (abril de 2012). La mayor parte de dicho censo se encuentra repartido en
Andalucía (22.25%), Extremadura (18.64%), Comunidad Valenciana (15.55%) y Castilla y
León (14.94%). Un 78.7 % del total de las colmenas de España pertenecen a apicultores
profesionales (dato de diciembre de 2012). Esto significa que pertenecen a explotaciones
apícolas con más de 150 colmenas (dato superior al del conjunto de la UE, que sólo
cuenta con un 32.8% de las colmenas en manos de apicultores profesionales).
Según DataComex, en 2012 el 89.8% de la miel importada por España procedía de
terceros países, especialmente China (14.232 toneladas), seguido por Argentina (587
toneladas), lo que supone 3.7% del total. Según la misma fuente, México habría
exportado a nuestro país en 2012 un total de 309 toneladas, lo que constituiría
únicamente un 2% del total.
Los principales destinos en la UE de las exportaciones de miel desde España en 2012
fueron Francia con 5.734 toneladas (34.8%), Alemania con 4.201 toneladas (25.5%), Italia
con 1.495 toneladas (9.1%) y Portugal con 1.114 toneladas (6.8%) (COAG, 2015).
Asia 46%
Europe22%
Africa10%
America20%
Oceania 2%
Introducción
31
Figura1.8.- Producción de miel en Europa en 2012 (en Toneladas). Datos según FAO
1.5.-Bibliografía
Accorti M., Piazza M.G. & Persano-Oddo L. (1987). La conductivité électrique et le
contenu en cendre du miel, Apiacta 22, 19–20.
Alcañiz M. (2011). Diseño de un sistema de lengua electrónica basado en técnicas electroquímicas voltamétricas y su aplicación en el ámbito agroalimentario. Tesis doctoral. Universidad Politécnica de Valencia. Departamento de Ingeniería Electrónica.
Álvarez-Suárez J.M., Giampieria F., González-Paramás A.M., Damianic E., Astolfid P. & Martínez-Sanchez G. (2012) Phenolics from monofloral honeys protect human erythrocyte membranes against oxidative damage. Food and Chemical Toxicology, 50 (5), 1508–1516.
Anderson D. L. & Trueman J. W. H. (2000). Varroa jacobsoni (Acari: Varroidae) is more than one species. Experimental & applied acarology, 24(3), 165-189.
Anklam E. (1998). A review of the analytical methods to determine the geographical and botanical origin of honey. Food chemistry, 63(4), 549-562.
Bargańska Ż., Ślebioda M. & Namieśnik J. (2013). Pesticide residues levels in honey from apiaries located of Northern Poland. Food Control, 31(1), 196-201.Beekman M., & Ratnieks F. L. W. (2000). Long‐range foraging by the honey‐bee, Apis mellifera L. Functional Ecology, 14(4), 490-496.
Beekman M. & Ratnieks F. L. W. (2000). Long-range foraging by the honey-bee, Apis mellifera L. Functional Ecology, 14: 490-496.
Belitz H. D. & Grosch W. (1997). Química de los Alimentos. Editorial Acribia. Zaragoza, España.
Bulgaria3%
Republica Checa3%
Francia4% Alemania
6%
Grecia5%
Hungria6%
Italia3%
Polonia4%
Portugal3%
Rumania8%
Serbia2%
España11%
Ukrania25%
Reino Unido2%
Resto12%
Introducción
32
Blasco C., Vázquez-Roig P., Onghena M., Masia A. & Picó Y. (2011). Analysis of insecticides in honey by liquid chromatography-ion trap-mass spectrometry: Comparison of different extraction procedures. Journal of Chromatography A, 1218, 4892-4901.
BOE 145.Orden de 12 de junio de 1986 por la que se aprueban los métodos oficiales de análisis para la miel.
Bogdanov S. & Martin P. (2002). Honey Authenticity: a Review. Swiss Bee Research Centre.
Bogdanov S., Martin P. & Lullmann C. (2002). Harmonised methods of the international honey commission. Swiss Bee Research Centre, FAM, Liebefeld.
Bogdanov S., Ruoff K. & Persano Oddo L. (2004). Physico-chemical methods for the characterisation of unifloral honeys: a review. Apidologie, 35(Suppl 1), 4-17.
Bogdanov S. (2011). The Honey Book. Bee Product Science. www.bee-hexagon.net.
Brookes S.T., Barrie A. & Davies J.E. (1991). A rapid 13C/12C test for determination of corn syrups in honey, J. Assoc. Off. Anal. Chem. 74, 627-629.
Cabañero A.I., Recio J.L. & Ruperez M. (2006). Liquid chromatography coupled to isotope ratio mass spectrometry: A new perspective on honey adulteration detection, Journal of agricultural and food chemistry 54, 9719-9727.
Calatayud F. & Simó E. Una Varroa cada vez más virulenta. Apitemas -apipatologia y sanidad.http://www.apiads.es/index.php/apitemas, Consultada en septiembre de 2015.
Campos I., Bataller R., Armero R., Gandía J. M., Soto J., Martinez-Mañez R. & Gil-Sánchez L. (2013).Monitoring grape ripeness using a voltammetric electronic tongue. Food Research International 54, 1369–1375.
Carrasco-Pancorbo A., Casado-TerronesS., Segura-Carretero A. & Fernández-Gutiérrez A. (2008). Reversed-phase high-performance liquid chromatography coupled to ultraviolet and electrospray time-of-flight mass spectrometry on-line detection for the separation of eight tetracyclines in honey samples. Journal of Chromatography A, 1195(1), 107-116.
Castro-Vazquez L., Diaz-Maroto M.C. & Pérez-Coello M.S. (2007). Aroma composition and new chemical markers of Spanish citrus honeys. Food Chemistry, 103, 601-606.
Castro-Vazquez L., Diaz-Maroto M.C., González-Viñas M.A. & Pérez-Coello M.S. (2009). Differentiation on monofloral citrus, rosemary, eucalyptus, lavender, thyme and heather honeys based on volatile composition and sensory descriptive analysis. Food Chemistry, 112, 1022-1030.
CCPR- Comité sobre Residuos de Plaguicidas del Codex Alimentarius, (2013). Consultada en noviembre de 2015. ftp://ftp.fao.org/Codex/Meetings/ccpr/ccpr45/pr45_12s.pdf
Celis M. & Díez M. J. (1995). Análisis polínico de mieles en la cabrera baja (montes de León, España). Acta Botanica Malacitana, 20: 91-96.
Introducción
33
Chen L., Xue X., Ye Z., Zhou J., Chen F. & Zhao J. (2011). Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy. Food Chemistry 128, 1110–1114.
Cherchi A., Spanedda L., Tuberoso C. & Cabras P. (1994). Solid-phase extraction and high-performance liquid chromatographic determination of organic acids in honey. Journal of Chromatography A, 669(1), 59-64.
COAG. Coordinadora de Organizacines de Agricultores y Ganaderos. Informes-Apicultura. Anuario Agrario 2015. Consultada en septiembre de 2015. http://www.coag.org/rep_ficheros_web/e7a475275366fdbc0bed13f9f2b9d198.pdf
Codex Alimentarius. (2001). Draft revised standard for honey. Council Directive 2001/110/EC of 20 December 2001 relating to honey. Official Journal of the European Communities L10, 47-52.
Commission Regulation (EU) N º 37/2010 of 22 December 2009 on pharmacologically
active substances and their classification regarding maximum residue limits in
foodstuffs of animal origin.
Cordella C., Antinelli J. F., Aurieres C., Faucon J. P., Cabrol-Bass D. & Sbirrazzuoli N. (2002). Use of differential scanning calorimetry (DSC) as a new technique for detection of adulteration in honeys. 1. Study of adulteration effect on honey thermal behavior. Journal of Agriculture Food Chemistry, 50, 203–208.
Cote J., Casabianca H., Chardon S., Lheritier J. &Grenier-Loustalot M. F. (2003). Application of carbohydrate analysis to verify honey authenticity. Journal of Chromatography A, 1021, 145–155.
Crane E. (1975). Honey: a comprehensive survey /edited by Eva Crane. London:
Heinemann for the Bee Research Association.
Crane E. (1990). Bees and Beekeeping. Scientific, Practice and World Resources. 1st ed.,
Heinemann Newnes, Halley Court, Jordan Hill, Oxford OX2 8EJ, London, UK.
DataComex -Estadísticas del Comercio Exterior–Ministerio de Economía y Competitividad. datacomex.comercio.es. Consultada en noviembre de 2015.
Dias L.A., Peres A.M., Vilas-Boas M., Rocha M.A., Estevinho L. & Machado A.A.S.C. (2008). An electronic tongue for honey classification, Microchim Acta, 163, 97–102.
Dias L.A., Peres A.M., Veloso A.C.A., Reis F.S., Vilas-Boas M. & Machado A.A.S.C. (2009). An electronic tongue taste evaluation: Identification of goat milk adulteration. Sensors and Actuators B: Chemical, 136 (1), 209–217.
Directiva 2001/110/CE del Consejo de 20 de diciembre de 2001 relativa a la miel. Comunidad Europea.
Directiva 2002/657/EC. Decisión de la comisión, por la que se aplica la Directiva 96/23/CE del Consejo en cuanto al funcionamiento de los métodos analíticos y la interpretación de los resultados.
Introducción
34
Directiva 2014/63/UE del Parlamento Europeo y del Consejo, de 15 de mayo de 2014, por la que se modifica la Directiva 2001/110/CE relativa a la miel
Del Baño Breis F. (2000). Mieles y Pólenes: palinoteca de la Región de Murcia.
De la Fuente E., Martinez-Castro I. & Sanz J. (2005). Characterization of Spanish unifloral honeys by solid phase microextraction and gas chromatography-mass spectrometry. Journal of Separation Science, 28, 1093–1100.
Diaz-Moreno A. (2009). La influencia de las condiciones de almacenamiento sobre la calidad físico-química y biológica de la miel. Tesis doctoral. Universidad de Zaragoza.
Elflein L. & Raezke K.P. (2005). Coupling of liquid chromatography with carbon stable isotope ratio mass spectrometry (LC-IRMS) – a promising new technique for detection of sugar adulteration of honey, Apimondia abstracts Ireland 2005, Apimondia International Apicultural Congress, Dublin, Ireland, 35-36.
Escriche I., Kadar M., Juan-Borrás M. & Domenech E. (2014). Suitability of antioxidant capacity, flavonoids and phenolic acids for floral authentication of honey. Impact of industrial thermal treatment. Food chemistry, 142, 135-143.
Escriche I., Kadar M., Juan-Borrás M. & Domenech E. (2011). Using flavonoids, phenolic compounds and headspace volatile profile for botanical authentication of lemon and orange honeys. Food Research International, 44, 1504-1513.
Escuredo O., Silva L., Valentao P., Seijo M.C. & Andrade P. (2012). Assessing Rubus honey value: pollen and phenolic compounds content and antibacterial capacity. Food Chemistry, 130 (3), pp. 671–678.
EU Pesticides database: http://ec.europa.eu/food/plant/pesticides. Consultada en
octubre 2015.
Eurachem (2014). The Fitness for Purpose of Analytical Methods ALaboratory Guide to
Method Validation and Related Topics.
Fallico B., Zappala M., Arena E. & Verzera A. (2004). Effects of conditioning on HMF
content in unifloral honeys. Food Chemistry, 85, 305–313.
Fattori S. (2004). La Miel: Propiedades, Composición Y Análisis Físico-Químico.
Apimondia.
Floris I., Satta A., Carpana E. & Sabatini A.G. (2002). The ethanol content as a useful
parameter for the identification of honey. Symposio II Ruol della Recerca in
Apicoltura, Bologna.
Gallina A., Stocco N. & Mutinelli F. (2010). Karl Fischer Titration to determine moisture in honey: A new simplified approach. Food control, 21(6), 942-944.
García-Breijo E. (2005). Diseño de un sistema multisensor en tecnología thick-film; fabricación, caracterización y estudio de su aplicabilidad a la medida de parámetros físico-químicos. Tesis doctoral. Universidad Politécnica de Valencia. Departamento de Ingeniería Electrónica.
Introducción
35
García-Chao M., Agruña M. J., Flores-Calvete G., Sakkas V., Llompart M. & Dagnac T. (2010). Validation of an off line solid phase extraction liquid chromatography-tandem mass spectrometry method for the determination of systemic insecticide residues in honey and pollen samples collected in apiaries from NW Spain. Analytica Chimica Acta, 672, 107-113.
Garcia-Pérez R. (2003). Estudio palinologico y colorimétrico de mieles monoflorales de la Región de Murcia.Proyecto final de carrera. Uniniversidad Politécnica de Cartagena.
Gheldof N., Wang X.H. & Engeseth N. (2002). Identification and quantification of antioxidant components of honeys from various floral sources. Journal of Agricultural and Food Chemistry, 50 (21), 5870–5877.
Gómez-Pajuelo A. (2004). Mieles de España y Portugal. Montagud editores. Barcelona,
González-Miret M. L., Terrab A., Hernanz D., Fernández-Recamales M. A. & Heredia F. J. (2005). Multivariate correlation between color and mineral composition of honeys and by their botanical origin. Journal of agricultural and food chemistry, 53(7), 2574-2580.
Hamdan K. (2010). Crystallization of Honey. Bee World, 87 (4), 71-74.
Hammel Y. A., Mohamed R., Gremaud E., LeBreton M. H. & Guy P. A. (2008). Multi-screening approach to monitor and quantify 42 antibiotic residues in honey by liquid chromatography–tandem mass spectrometry. Journal of Chromatography A, 1177(1), 58-76.
Huidobro J.F., Rea M.E, de Andrade P.C.B., Sanchez M.P., Sancho M.T., Muniategui S. & Simal-Lozano J. (1994). Enzymatic determination of primary alcohols as apparent ethanol content in honey. J. Agr. Food Chem. 42, 1975-1978.
ICH (2005).Validation of Analytical Procedures: Methodology, adoptedin 1996, Geneva Q2B, in 2005 incorporated in Q2(R1).
ISO 5496 (2006). Sensory Analysis. Methodology. Initiation and training of assessors in the detection and recognition of odours.
Jiménez C., Muñoz F. J., Bratov A. & Domínguez C. (2002). Lenguas electrónicas: sensores químicos aplicados a la medida del sabor y al control de calidad de los alimentos, Percepnet, 13, 1697-4131, Octubre.
Kadar M., Juan Borrás M., Carot J. M., Doménech E. & Escriche I. (2011).Volatile fraction composition and physicochemical parameters as tools for the differentiation of lemon blosson honey and orange blosson honey. Journal of the Science of Food and Agriculture, 91, 2768 – 2776.
Kaufmann A., Roth S., Ryser B. & Widmer M. (2002). Quantitative LC/MS-MS
determination of sulfonamides and some other antibiotics in honey. Journal of
AOAC International, 85 (4), 853-860.
Kelly J. D., Petisco C. & Downey G. (2006). Application of Fourier transform midinfrared spectroscopy to the discrimination between Irish artisanal honey and such honey
Introducción
36
adulterated with various sugar syrups. Journal of agricultural and food chemistry, 54(17), 6166-6171.
Kerkvliet J.D. & Meijer H.A.J. (2000). Adulteration of honey: relation between microscopic analysis and deltaC-13 measurements, Apidologie 31, 717-726.
Khong S. P., Hammel Y. A. & Guy P. A. (2005). Analysis of tetracyclines in honey by high‐
performance liquid chromatography/tandem mass spectrometry. Rapid
communications in mass spectrometry, 19(4), 493-502.
Kujawski M. W. & Namiesnik J. (2008). Challenges in preparing honey samples for
chromatographic determination of contaminants and trace residues. Trends in
Analytical Chemistry, Vol. 27 (9), 785-793.
Ledoux M. N., Pernal S. F., Higo H. A. & Winston M. L. (2000). Development of a bioassay
to test the orientation behaviour of the honey bee ectoparasite, Varroa jacobsoni.
Journal of Apicultural Research, 39(1-2), 47-54.
Louveaux J., Maurizio A. &Vorwohl G. (1978). Methods of Melissopalynology. Bee World,
59(4), 139-157.
Low N.H. & South W. (1995). Determination of honey authenticity by capillary gas chromatography, J. Assoc. Off. Anal. Chem. 78, 1106-1113.
Martínez Máñez R., Soto J., García-Breijo E., Gil L., Ibáñez J. & Llobet E. (2005). An
“electronic tongue” design for the qualitative analysis of natural waters. Sensors
and Actuators B: Chemical, 104(2), 302–307.
Maudens K. E., Zhang G. F. & Lambert W. E. (2004). Quantitative analysis of twelve
sulfonamides in honey after acidic hydrolysis by high-performance liquid
chromatography with post-column derivatization and fluorescence detection.
Journal of Chromatography A, 1047(1), 85-92.
Meda A., Lamien C.E., Romito M., Millogo J. & Nacoulma O.G. (2005). Determination of
the total phenolic, flavonoid and proline contents in Burkina Fasan honey, as well
their radical scavenging activity. Food chemistry, 91(3), 571-577.
Mundo Apícola. Loque Europea, http://www.mundoapicola.com/pdf/loqueeuropea.pdf.
Consultada en Mayo 2012.
National Honey Board. (www.honey.com). Consultada en septiembre de 2015.
Oroian M. & Escriche I. (2015). Antioxidants: Characterization, natural sources, extraction
and analysis. Food Research International, 74, 10-36.
Oyarzun M.T., Figueroa A. & Tartanac F. (2005). Oportunidades de mejoramiento en la
calidad e inocuidad de la cadena productiva de la miel en Chile. Organización de las
Naciones Unidas para la Agricultura y la Alimentación, Santiago de Chile.
Pang G. F., Cao Y. Z., Zhang J. J., Jia G. Q., Fan C. L. & Li X. M. (2005). Simultaneous
Determination of Oxytetracycline, Tetracycline, Chlortetracycline and Doxycycline
Introducción
37
in Poultry Meat by High Performance Liquid Chromatography. Journal of
Instrumental Analysis, 24(4), 61.
Pang G. F., Fan C. L., Liu Y. M., Cao Y. Z., Zhang J. J., Fu B. L. & Wu Y. P. (2006). Multi-
residue method for the determination of 450 pesticide residues in honey, fruit juice
and wine by double-cartridge solid-phase extraction/gas chromatography-mass
spectrometry and liquid chromatography-tandem mass spectrometry. Food
additives and contaminants, 23(8), 777-810.
Parlamento Europeo (2013). Report on the food crisis, fraud in the food chain and the
control thereof [2013/2091(INI)].
Persano-Oddo L. & Piro R. (2004). Main European unifloral honeys: descriptive sheets,
Apidologie 35 (Suppl. 1), S38–S81.
Persano-Oddo L. & Bogdanov S. (2004). Determination of honey botanical origin:
problems and issues. Apidologie 35, S2–S3.
Piana L., Persano-Oddo L., Bentabol A., Bruneau E., Bogdanov S. &Guyot S. (2004).
Sensory analysis applied to honey: state of the art. Apidologie, 35, S26–S37.
Pirini A, Conte L.S, Francioso O. & Lercker G. (1992). Capillary gas chromatographic determination of free amino acids in honey as a means of discrimination between different botanical sources. Journal of High Resolution Chromatography, 15,165–170.
Raude-Roberg L. (1994). Analyse der Saccharide spaltenden Enzyme des Bienenhonigs,
Ph.D. thesis, Niedersächsisches Institut für Bienenkunde, Celle, Germany.
Radovic B.S., Careri M., Mangia A, Musci M., Gerboles M. & Anklam E. (2001). Contribution of dynamic headspace GC–MS analysis of aroma compounds to authenticity testing of honey. Food Chemistry, 72 (4), 511–520.
Real Decreto 1049/2003, de 1 de agosto, por el que se aprueba la Norma de calidad relativa a la miel.
Reglamento (CE) 396/2005 del parlamento europeo y del consejo de 23 de febrero de 2005 relativo a los límites máximos de residuos de plaguicidas en alimentos y piensos de origen vegetal y animal.
Reglamento 37/2010 (UE). Relativo a las sustancias farmacológicamente activas y su clasificación por lo que se refiere a los límites máximos de residuos en los productos alimenticios de origen animal. Diario Oficial de la Comunidad Europea, L15, 1-72.
Rial-Otero R., Gaspar E. M., Moura I. & Capelo J. L. (2007). Chromatrographic-based
methods for pesticide determination in honey: An overview. Talanta, 71, 503-514.
Rodriguez-Otero J. L., Paseiro P., Simal J. & Cepeda A. (1990). Intento de caracterización
de las mieles naturales de Galicia mediante las fracciones proteicas separadas por
electroforesis. In Anales de Bromatologia (Vol. 42, No. 1, pp. 83-98). Sociedad
Española de Bromatologia.
Introducción
38
Rodriguez-Otero J. L., Paseiro P., Simal J., Terradillos L. & Cepeda A. (1995). Silicon,
phosphorus, sulphur, chlorine and ash contents of Spanish commercial
honeys.Zeitschrift für Lebensmittel-Untersuchung und Forschung, 200(3), 233-234.
Ropa D. (2010). Comparison of Mineral and Enzyme Levels in Raw and Processed Honey.
Wisconsin: Ropa Science Research.
Rossmann A., Lüllmann C. & Schmidt H.L. (1992). Massenspektrometrische Kohlenstoff-
und Wasserstoff-Isotopen-Verhältnismessung zur Authentizitätsprüfung bei
Honigen, Zeitschrift für Lebensmittel-Untersuchung und -Forschung 195, 307-311.
Sáenz C. & Gómez C. (2000). Mieles españolas. Características e identificación mediante
el análisis del polen. Ediciones Mundi-Prensa. Madrid (España).
Sammataro D. & Finley J. (2004).Obervations of the ectoparasitic bee mite Varroa destructor in honey bee (Apis mellifera) cells infected with chalkbrood (Ascosphaera apis). Journal of Apicultural Research, 43(1), 28-30.
Sancho M. T., Muniategui S., Huidobro J. F. & Simal Lozano J. (1992). Aging of honey. Journal of Agricultural and Food Chemistry, 40(1), 134-138.
Schneider A. (2011). Ultra-filtering Removes Pollen, Hides Honey Origins. http://www.foodsafetynews.com/2011/11/tests-show-most-store-honey-isnt-honey/#.Vky44XYve01. Consultada en noviembre de 2015.
Serra-Bonvehí J. & Ventura F. (1995). Characterization of Citrus honey produced in Spain. Journal of Agricultural and Food Chemistry, 43, 2053–2057.
Sesta G., Piana L, Persano-Oddo L., Lusco L. & Belligoli P. (2008). Methyl anthranilate in Citrus honey. Analytical method and suitability as a chemical marker. Apidologie, 39 (3), 334-342.
Sevimli H., Bayulgen N. & Varinlioglu A. (1992). Determination of trace elements in honey by INAA in Turkey. Journal of radioanalytical and nuclear chemistry, 165(5), 319-325.
Silici S., Sagdic O. & Ekici L. (2010). Total phenolic content, antiradical, antioxidant,
andantimicrobial activities of Rhododendron honeys. Food Chem., 121, 238-243.
Sivakesava S. & Irudayaraj J. (2001). A rapid spectroscopic technique for determining honey adulteration with corn syrup. Journal of Food Science, 66(6), 787-791.
Simon-Delso N., Amaral-Rogers V. & Belzunces L.P. (2015). Systemic insecticides (neonicotinoids and fipronil): trends, uses, mode of action and metabolites. Environmental Science and Pollution Research International 22, 5-34.
Simsek A., Bilsel M. & Goren A. C. (2012). 12C/ 13C pattern of honey from Turkey and determination of adulteration in commercially available honey samples using EA-IRMS. Food Chemistry, 130, 1115-1121.
Smanalieva J. & Senge B. (2009). Analytical and rheological investigations into selected unifloral German honey. Eur Food Res Technol, 229,107–113.
Introducción
39
Soderlund D.M. (2012). Molecular Mechanisms of Pyrethroid Insecticide Neurotoxicity: Recent Advances. Archives of Toxicology, 86 (2), 165-181.
Soria A. C., Martínez‐Castro I. & Sanz J. (2003). Analysis of volatile composition of honey by solid phase microextraction and gas chromatography‐mass spectrometry. Journal of Separation Science, 26(9‐10), 793-801.
Ståhlberg T., Fu W., Woodley J. M. & Riisager A. (2011). Synthesis of 5‐(Hydroxymethyl) furfural in Ionic Liquids: Paving the Way to Renewable Chemicals. ChemSusChem, 4(4), 451-458.
Taner G. & Czerwenka C. (2011). LC-MS/MS Analysis of Neonicotinoid Insecticides in Honey: Methodology and Residue Findings in Austrian Honeys J. Agric. Food Chem., 59, 12271–12277.
Tomás-Barberán F.A, Ferreres F., Ortiz A. & Fernández M.C. (1994). Estudio sobre el contenido en flavonoides de las Mieles de La Alcarria: su aplicación a la caracterización Geográfico-Botánica. Convenio de colaboración entre el Consejo Superior de Investigaciones Científicas y la Junta de Comunidades de Castilla-La Mancha.Ed, CSIC. ISBN: 8460618323.
Tosun M. (2013). Detection of adulteration in honey samples added various sugar syrups with 12C/13C isotope ratio analysis method. Food Chemistry, 138, 1629-1632.
Tsai W. H., Chuang H. Y., Chen H. H., Wu Y. W., Cheng S. H., & Huang T. C. (2010).
Application of sugaring-out extraction for the determination of sulfonamides in
honey by high-performance liquid chromatography with fluorescence detection.
Journal of Chromatography A, 1217(49), 7812-7815.
Van Engelsdorp D., Evans J., Saegerman C., Mullin C., Haubruge E, Nguyen B.K., Frazier
M., Frazier J., Cox-Foster D., Chen Y., Underwood R., Tarpy D.R. & Pettis J.S.
(2009). Colony Collapse Disorder: A Descriptive Study. Available at:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0006481.
Visquert M. (2015).Tesis Doctoral. Influencia de las condiciones térmicas en la calidad de la miel. Universidad Politécnica de Valencia.
Viñas P., Balsalobre N., López-Erroz C. & Hernández-Córdoba M. (2004). Liquid chromatography with ultraviolet absorbance detection for the analysis of tetracycline residues in honey. Journal of Chromatography A, 1022(1), 125-129.
Von Der Ohe W, Persano-Oddo P., Piana L, Morlot M. & Martin P. (2004). Harmonized methods of melissopalynology. Apidologie, 35, S18–S25.
White Jr. (1978). Advances in Food Research Volume 24, Pag iii-vii, 1-378. Capitulo: Honey Original Research Article, 287-374, ISBN: 978-0-12-016424-0. Edited by C.O. Chicheste.
White J. W., Riethof M. L., Subers M. H. & Kushnir I. (1962). Composition of American honeys. Tech Bull. U.S. Dep. Agric., 1261.
Introducción
40
White J.W. (1992). Internal standard stable carbon isotope ratio method for determination of C-4 plant sugars in honey: Collaborative study, and evaluation of improved protein preparation procedure, J. AOAC Int, 75, 543-548.
White J.W. & Bryant V.M. (1996). Assessing Citrus honey quality: pollen and methyl anthranilate content. Journal of Agricultural and Food Chemistry, 44, 3423–3425.
White J.W., Winters K., Martin P. & Rossmann A. (1998). Stable carbon isotope ratio analysis of honey: Validation of internal standard procedure for worldwide application. Journal of AOAC International, 81, 610-619.
White J.W. (2000). Isotope ratio testing of honey: Demystifying the internal standard test. Am. Bee J. 140, 318-321
Wilkins A. L. & Lu Y. (1995). Extractives from New Zealand honeys. 5. Aliphatic dicarboxylic acids in New Zealand rewarewa (Knightea excelsa) honey. Journal of Agricultural and Food Chemistry, 43(12), 3021-3025.
Williams D. L. (2000). A veterinary approach to the European honey bee (Apis mellifera). The Veterinary Journal, 160(1), 61-73.
Williamson S. M. & Wright G. A. (2013). Exposure to multiple cholinergic pesticides impairs olfactory learning and memory in honeybees. The Journal of experimental biology, 216(10), 1799-1807.
Yao L., Jiang Y., Singanusong R., Datta, N. & Raymont K. (2004). Phenolic acids and abscisic acid in Australian Eucalyptus honeys and their potential for floral authentication. Food Chemistry, 86(2), 169-177.
Zamora M.C. & Chirife J. (2006). Determination of water activity change due to crystallization in honeys from Argentina. Food Control, 17 (1), 59–64.
Zhuiykov S. (2012). Solid-state sensors monitoring parameters of water quality for the next generation of wireless sensor networks. Sensors and Actuators B: Chemical, 161 (1), 1-20.
2. OBJETIVOS
Objetivos
43
2. Objetivos
2.1.-Objetivo general
La presente tesis doctoral plantea dos objetivos generales:
2.1.1. Evaluar las técnicas que se vienen utilizando de forma rutinaria en el control de
calidad de mieles, tanto a nivel industrial como comercial, y compararlas con otras
alternativas no convencionales.
2.1.2. Evaluar la efectividad del control de la materia prima (llevado a cabo en la etapa
de recepción industrial) para cumplir los límites legales establecidos en lo referente a la
presencia de residuos químicos en miel. Además, valorar el riesgo para el consumidor
como consecuencia de la exposición a dichos residuos cuando legalmente tenga
establecido un LMR (Límite máximo de residuos).
2.2.-Objetivos específicos
2.2.1. Analizar en la etapa de recepción de las industrias de envasado de miel, la materia
prima mediante las técnicas de rutina llevadas a cabo por la industria para la clasificación
y control de calidad de las mismas.
2.2.2. Evaluar la influencia del tipo de miel, año de cosecha y prácticas apícolas en la
variabilidad de los valores obtenidos mediante las técnicas de rutina.
2.2.3. Evaluar la efectividad del análisis de metilantranilato como técnica
complementaria en la clasificación botánica de mieles españolas de cítrico.
2.2.4. Estudiar la capacidad discriminatoria de una lengua potenciométrica, desarrollada
a partir de una combinación de electrodos metálicos, para la diferenciación de mieles.
Objetivos
44
2.2.5. Estudiar la capacidad discriminatoria de las técnicas convencionales (polínico,
color, químicas y fisicoquímicas) para la clasificación de mieles de diferentes
procedencias botánicas y geográficas.
2.2.6. Estudiar la capacidad discriminatoria del análisis de la fracción volátil para la
clasificación de mieles de diferentes procedencias botánicas y geográficas.
2.2.7. Evaluar la presencia de sulfamidas en muestras de miel procedentes de la etapa
de recepción de diferentes industrias envasadoras de miel, así como en muestras
comerciales procedentes de las mismas industrias.
2.2.8. Cuantificar la presencia de diferentes pesticidas provenientes de tratamientos
veterinarios o agrícolas, en mieles milflores comerciales (marcas blancas y líderes del
mercado).
2.2.9. Evaluar la influencia del origen geográfico en la presencia de pesticidas en mieles
milflores comerciales.
2.2.10. Evaluar el riesgo total para el consumidor en relación a la presencia de pesticidas
en mieles.
3. RESULTADOS
Marisol Juan-Borrás, Angela Periche, Eva Domenech, Isabel Escriche
Journal of Chemistry, (2015).http://dx.doi.org/10.1155/2015/929658
3.1. Physicochemical quality parameters at the reception stage of
the honey packaging process: Influence of type of honey, year of
harvest and beekeeper
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
49
Abstract
The aim of this paper was to evaluate the influence of the type of honey, year of collection
and the beekeeper on the main physicochemical quality parameters
(hydroxymethylfurfural “HMF”, moisture and colour), measured on reception of the raw
honey. 1593 samples (11 types of honey categorized by means of pollinic analysis),
provided by 98 beekeepers, from 2009 to 2013, were analyzed. Colour was the parameter
most affected by the type of honey and year, whereas HMF was the least affected in both
cases. The clearest honeys were found to have the greatest moisture (orange, rosemary
and lemon) and the darkest had the least moisture (lavender stoechas, eucalyptus,
sunflower, honeydew and retama). Lavender, polyfloral and thyme had intermediate
values of these parameters. For moisture, most samples were in accordance with
international requirements (less than 20 g/100 g). All values were below the required
limit for HMF (40mg/kg), although a few of them were abnormally high as they were raw
honeys (i.e. 2% of the samples had values higher than 20 mg/kg). The fact that all the
inadequate samples came from specific beekeepers, highlights the importance of their
role, suggesting that training in good practices is the key to guarantee honey quality
before it reaches the industry.
Keywords: honey quality, HMF, moisture, colour, year of harvest, beekeeper
1. Introduction
Food products have to satisfy numerous quality criteria before commercialization,
especially in industrialized countries, where there is a need for high quality products with
well-defined characteristics. Honey is not an exception and must be delivered to the
consumer with its essential composition and quality minimally altered with respect to
freshly harvested honey (Codex Alimentarius, 2001). There are international and
sometimes local regulations that specify honey quality (Council Directive 2001/110;
DOGV No. 4167/2002). Therefore, on receiving batches of raw honey the packaging
industry has to carry out a wide range of analyses, such as quantification of pollen and
physicochemical parameters (hydroxymethylfurfural “HMF”, moisture and colour, among
others). There are two main reasons for this: 1)to facilitate the classification of honeys
according to their botanical origin (considering the pollinic percentage and colour) and 2)
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
50
to meet the legislated mandatory requirements during commercialization (e.g. HMF
content less than 40 mg/kg or moisture content less than 20g/100g)(Council Directive
2001/110).
Honey classified as unifloral always has a higher commercial value than polyfloral. For this
reason, the industry realizes this task prior to packaging, when according to information
provided by the beekeeper there is a reasonable probability that a honey can be classified
as unifloral. The identification and quantification of the percentage of pollen by
microscopic examination is used to authenticate the botanical origin of honey (Von Der
Ohe et al., 2004; Escriche et al., 2012; Panseri et al., 2013). The colour of honey is directly
related to the botanical source of the nectar, and therefore can help in the classification
of unifloral honeys. In addition, this parameter has commercial value as it is used as a
criterion of acceptance or rejection by the consumers; however, it is only regulated by
some Quality Marks (DOGV No. 4167/2002).
HMF is the most consistent indicator of honey freshness as it is practically absent in
freshly harvested honey. However, it increases during handling, extraction, conditioning
or storage operations, and also, as a consequence of the liquefaction and pasteurization
carried out to improve manageability and destroy the crystallization nuclei (Visquert et
al., 2014). Honey packaging plants must be very demanding about the HMF content of
raw honey, as they are obliged by law to comply with the requirement established for
this parameter during the best-before-date printed on the label. The moisture content of
honey depends on the season in which it is harvested, the climatic conditions and the
good practices carried out by the beekeepers (Persano-Oddo & Piro, 2004). This
parameter has a decisive influence on viscosity, flavour and palatability, but overall on
crystallization and fermentation (Turhana et al., 2008). These two alterations modify the
appearance, and therefore contribute to customer rejection, consequently causing losses
to the industry.
The honey packaging industry realises the importance of legal compliance but also the
necessity of providing consumers with consistent quality. When the process is controlled
carefully, the key to a quality end product lies in the good quality of the raw material.
Knowledge about the origin of the variability of the critical parameters of physicochemical
quality is essential to take measures to improve the quality of the raw honey.
Consequently, the probability that the commercialized honey does not meet the required
specifications will be reduced. Therefore, the objective of this paper was to evaluate the
influence of the type of honey, year of collection and the beekeeper’s role on the main
physicochemical quality parameters analysed (HMF, moisture and colour) on reception
in packaging companies.
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
51
2. Materials and Methods
2.1.-Honey samples
A total of 1593 samples of raw honey, collected over five years (from 2009 to 2013) in
the routine checks that take place on reception of raw honey, were analysed. These
samples came from four commercial organizations (provided by 98 beekeepers) located
in the Valencian region (Spain). The samples represented the most common varieties
available in Spain: 231 (14.5%) orange blossom (Citrus spp.); 111 (7%) lemon blossom
(Citrus limon); 216 (13.6%) rosemary (Rosmarinus officinalis); 135 (8.5%) sunflower
(Helianthus annuus); 34 (2.1%) thyme (Thymus spp.);27 (1.7%) lavender (Lavandula spp.);
14 (0.9%) lavender stoechas (Lavandula stoechas); 26 (1.6%) retama (Lygos
sphaerocarpa); 76 (4.8%) eucalyptus (Eucalyptus spp.); 117 (7.3%) honeydew honey, and
605 (38%) polyfloral.
The botanical categorization of all the batches was carried out by means of pollinic
analysis.
2.2.-Analytical Determinations
2.2.1. Melissopalynological analysis
The percentage of pollen was obtained for each sample following the recommendations
of the International Commission for Bee Botany (Von Der Ohe et al., 2004). Microscopic
examination was carried out at the magnification that was most suitable for identifying
the various elements in the sediment (400 to 1000×). A light microscope (Zeiss Axio
Imager, Göttingen, Germany) at a magnification power of x 400 with DpxView LE image
analysis software attached to a DeltaPix digital camera was used.
2.2.2. Physicochemical and colour analysis
Hydroxymethylfurfural content “HMF” (White method), and moisture content were
analyzed in accordance with the Harmonized Methods of the European Honey
Commission (Bogdanov, 2002). Colour was determined using a millimetre Pfund scale C
221 Honey Color Analyzer (Hanna Instruments). All tests were performed in triplicate.
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
52
2.3. Statistical analyses
A multifactor analysis of variance (ANOVA) (using Statgraphics Centurion for Windows)
was applied to study the influence of the type of honey and the year of harvesting on the
HMF, moisture and colour. LSD contrast (least significant difference) with level of
significance = 5% was used to analyse the differences between means. A multiple
correspondence analysis was applied using the statistical software SPAD (version 6.0), to
group types of honey based on the quality parameters analysed and also to better
understand the relationship between the parameters and the honeys.
3. Results and Discussion
3.1.-Botanical origin
The first step in this study was to carry out the botanical categorization of all the batches
by means of pollinic analysis. As an example, Figure 1 shows several pictures
corresponding to the predominant pollen present in each type of unifloral honey. Next
to each botanical name, the minimum percentage of pollen required to classify a honey
as belonging to a specific botanical genus considered in the present work is shown. These
values represent the minimums commonly used in the industry and recommended by
different authors and Quality Marks (DOGV No. 4167/2002; Von Der Ohe et al., 2004;
Persano-Oddo & Piro, 2004; Saenz-Laín & Gómez-Ferreras, 2000; Persano-Oddo &
Bogdanov, 2004; Gómez-Pajuelo, 2004; Soria et al., 2004). Honey was classified as
honeydew, if the ratio of honeydew elements to that of pollen grains exceeded 3, and
the conductivity was higher than 0.8mS/cm (Council Directive 2001/110).Finally, honey
was classified as polyfloral, if it did not contain sufficient pollen from a specific botanical
species.
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
53
3.2.-Influence of type of honey and year of harvest on the
physicochemical parameters
Table 1 shows the descriptive statistics (mean and standard deviation) of HMF, moisture
and colour parameters analysed considering the type of honey and year of harvest. In
addition, this table shows the ANOVA results (F-ratio and significant differences) obtained
for these two factors. The interaction between both factors was not significant.
Considering that the higher the F-ratio, the greater the effect that a factor has on a
variable, colour was the parameter most affected by the factors “type of honey” and
“year”, whereas HMF was the least affected in both cases, as was expected.
A Kolmogorov-Smirnov test (data not shown) demonstrated that data related to moisture
and colour were normally distributed; however, the HMF data were not normally
distributed as their descriptive statistics showed a strong positive skew (Skewness
coefficient=2.7) and a high positive kurtosis (Kurtosis coefficient=10.1).
HMF ranged between a minimum value of <0.5 mg/kg (data that are present in all
varieties) and a maximum of 37.4mg/kg, 37.9mg/kg and 39.8 mg/kg in sunflower honey,
orange blossom honey and polyfloral honey, respectively. Although all values were below
the required limit of 40mg/kg, it is obvious that some of them would be considered as
unacceptable as they were raw honeys. It should be noted that these outlying values are
not frequent, in fact, only 2% of samples had values higher than 20 mg HMF/kg, and less
than 0.5%of samples had values higher than 30 mg HMF/kg. In contrast with the before
mentioned type of honeys, retama, eucalyptus, lavender stoechas and lemon honeys had
maximum values of 7.4, 9.7, 10.2 and 10.3, respectively.
With respect to the moisture, it can be observed (Table 1) that a high value of this
parameter is characteristic of certain types of honey, such as thyme and rosemary with
average values equal or higher than 19.5 g/100 g. Some specific batches of orange
blossom, lemon tree, rosemary, sunflower, thyme, lavender, honeydew and polyfloral
honeys also exceeded 20g/100g moisture. On the contrary, this percentage was quite low
in some varieties such as lavender stoechas, retama and eucalyptus. In general, unifloral
honeys show some typical differences in water content depending on season and climate
(Persano-Oddo & Piro, 2004). Taking into account the fact that the moisture content of
honey has to be lower than 20g/100g (Council Directive 2001/110), the values obtained
in this work were not always satisfactory for the honey packaging industry.
With regard to colour, the results shown in this work were as expected for these varieties
of honey (Piazza & Persano-Oddo, 2004). A large range of variation between the
minimum and maximum was observed. Logically, the greatest difference was detected in
polyfloral honey with a range of 7 to 130 mm Pfund. Some types of honey such rosemary,
lemon and orange are in general characterized by a light colour which makes them highly
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
54
valued commercially. However, these honeys were sometimes darker than is
commercially desirable, ranging from 0.1 and 2 mm, to 50, 69, 62 mm, respectively. The
color of these types of honey can sometimes be strongly influenced by the nectar of other
flowers that bees do to sip. On the contrary, honeydew honey and retama honey were in
general the darkest (among the monovarietal honeys), reaching up to 120 mm on the
Pfund scale. In these types of honey the dark colour is traditionally a highly valued
characteristic. Furthermore, today it is well known that the darker a honey is, the higher
the nutritional value, due to the high mineral content and the antioxidant activity
(Persano-Oddo et al., 2008; Tornuka et al., 2013).
In relation to colour, although no limits are established by the general regulations
(Council Directive 2001/110), specific quality marks limit values according to varieties,
e.g. for citrus and rosemary less than 30mm Pfund in Valencian region regulation (DOGV
No. 4167/2002) and less than 30 and 35mm Pfund, respectively in Granada PDO (BOE
24621/2002); for lavender stoechas and thyme values must be above 50 and 55 mm
Pfund respectively in Granada PDO.
In order to detect possible grouping of the types of honeys according to the quality
parameters evaluated, a multiple correspondence analysis was carried out (Figure 2)
(Preys, 2007). Due to the requirements of the analysis, quality parameters were coded
categorically as intervals. The parameter values were: moisture (g/100g)[<16, 16-17, 17-
18, 18-19, 19-20, >20]; colour (Pfund scale)[<20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-
80, >80]; hydroxymethylfurfural [<0.50, 0.51–1.00, 1.01–1.50, 1.51-2.00, 2.01-2.50, 2.51-
3.50, 3.51-5.00, 5.01-6.50, 6.51-10.00, >10]. In addition, information about the variety of
honey was projected on the biplot in order to link this factor to quality parameter
intervals.
The projection of the varieties on the graph showed three clear groups: left (orange
blossom, lemon and rosemary); centre (thyme, lavender and polyfloral) and right
(sunflower, lavender stoechas, retama, eucalyptus and honeydew). These groups are also
related to certain categories of the quality parameters: the clearest honeys with the
highest moisture on the left and the darkest ones with the least moisture on the right.
The remaining honeys, with intermediate values of these parameters, are located in the
central area of the graph.
The second axis distinguishes samples depending on whether values of
hydroxymethylfurfural are close to the maximum, the minimum, or remain around
intermediate values. In this way, minimum values of HMF are located at the top of the
Figure 2 (<0.50 HMF), whereas maximum values can be found at the bottom (>10 HMF)”
(9.1%). In both cases these categories have a high relative contribution to this second
axis, as shown in brackets. The remaining categories of HMF are distributed between
these two reference points, but this distribution is not progressive along the axis.
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
55
Figure 1.- Pictures corresponding to the predominant pollen present in each type of unifloral honey and the minimum percentage of pollen required to classify a honey as belonging to a specific botanical genus considered in the present work.
Citrus sinensis (>10 % pollen) Citrus limon (>10 % pollen)
Rosmarinus officinalis (>10% pollen
Helianthus annuus (>45% pollen) Thymus sp. (>12% pollen)
Lavandula latifolia (>12% pollen)
Lavandula stoechas (>12% pollen ) Lygos sphaerocarpa (>35% pollen )
Eucaliptus sp. (>70% pollen)
Honeydew
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
56
Table 1. Descriptive statistics for moisture, colour and HMF for each variety of honey and year of harvesting.ANOVA results (F-ratio and
significant differences) obtained for two factors: type of honey and yearof harvest.
Type of honey HMF (mg/kg) Moisture (g/100g) Colour (Pfund scale)
Mean SD Min Max Mean SD Min Max Mean SD Min Max
Orange blossom 4.6de 6.4 <0.5 37.9 18.7c 1.3 15.5 23.3 27a 17 2 62
Lemon tree 2.1ab 2.3 <0.5 10.3 18.3b 1.5 15.0 21.6 26a 14 1 69
Rosemary 3.2bc 3.2 <0.5 27.3 19.9d 2.1 15.0 25.0 25a 15 0 50
Sunflower 3.3bc 4.3 <0.5 37.4 16.7a 1.3 14.6 21.2 70d 10 38 72
Thyme 4.1bcd 7.2 <0.5 35.6 19.5d 1.6 16.2 22.9 66cd 21 18 90
Lavender 6.1d 6.2 <0.5 22.9 17.9ab 2.2 15.7 25.2 48b 21 6 91
Lavender stoechas 3.4abcde 3.5 <0.5 10.2 16.5a 1.0 15.2 18.1 66cd 13 36 78
Retama 1.0a 1.7 <0.5 7.4 16.4a 0.7 14.7 17.5 84e 10 63 120
Eucalyptus 3.8cd 2.7 <0.5 9.7 16.6a 0.8 15.1 19.8 70d 11 35 89
Honeydew 4.5de 3.1 <0.5 27.2 16.5a 1.4 13.9 22.0 90e 13 57 127
Polyfloral 4.5cd 4.8 <0.5 39.8 17.7ab 1.8 14.8 24.1 64c 19 7 130
ANOVA F ratio 4.42*** 49.0*** 221***
Year of harvest HMF(mg/kg) Moisture(g/100g) Colour(Pfund scale)
Mean SD Min Max Mean SD Min Max Mean SD Min Max
2009 3.1a 3.4 <0.5 19.4 17.1ª 1.5 14.8 24.0 72e 18 13 130
2010 5.0c 5.9 <0.5 37.9 18.0bc 1.7 15.0 23.6 62d 24 10 127
2011 4.0b 4.8 <0.5 37.4 18.2cd 2.0 13.9 25.0 51c 27 2 125
2012 2.9a 3.6 <0.5 26.8 18.4d 1.9 15.5 25.2 47b 27 3 120
2013 4.1bc 6.2 <0.5 39.8 17.9b 1.7 14.2 22.1 40ª 26 0 110
ANOVA F ratio 7.14*** 17.32*** 55.94***
***p<0.001. For each factor, different letters in each column indicate homogeneous groups (significant differences at 95% confidence level as
obtained by the LSD test)
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
57
Figure 2.- Multiple correspondence analyses of the quality parameters coded as intervals with projected varieties of honey. M: moisture; C:colour; HMF: hydroxymethylfurfural.
3.3.-Influence of the beekeeper on the physicochemical
parameters
In order to evaluate the influence of the beekeeper on the physicochemical parameters,
the six most abundant honey types analysed in this work were considered (see section
2.1): orange blossom, lemon blossom, rosemary, sunflower, honeydew and polyfloral. An
ANOVA was carried out for every type of honey and every physicochemical parameter,
considering the factor beekeeper (Table 2). For this statistical analysis, beekeepers who
contributed less than 10 batches to the study were not considered. It can be observed
that the beekeeper has a significant influence on all the parameters evaluated and on all
the types of honey studied.
In relation to the quality parameters, HMF and moisture, although only a few values
exceeded the recommended limits these values came from specific types of honey and
beekeepers. What is clear is that if some beekeepers can work very well, which is
reflected in low values of both parameters, others should be able to also.
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
58
Colour differences are more justifiable in comparison to those observed for the before
mentioned parameters. That is to say, for the same honey type the accompanying flora
has a large influence on the variations in colour. However, it is important to note that
beekeepers also have an influence on this parameter since they are responsible for the
mix of the types of honey when cutting the honey from the honeycomb.
Due to the fact that beekeepers have a key role in honey quality parameters, it is
important to monitor their handling practices, although this is not always possible if they
operate in distant countries.
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
59
Table 2. Influence of the beekeeper on the physicochemical parameters (HMF, moisture and colour) and different types of honey.ANOVA
results (F-ratio and significant differences) for the factor: beekeeper.
Type of Honey HMF (mg/kg) Moisture (g/100g) Colour (Pfund scale)
Mean (SD) Min-Max ANOVA
F ratio
Mean (SD) Min-Max ANOVA
F ratio
Mean (SD) Min-Max ANOVA
F ratio
Orange blossom 5.5 (7.8) <0.5-37.8 15.3*** 19 (1) 16-23 4.3*** 26 (16) 2-89 5.48***
Lemon tree 2.2 (2.4) <0.5-10.3 19.2*** 18 (1) 15-22 11.2*** 27 (14) 1-69 2.35*
Rosemary 19.9 (2.1) <0.5-27.3 9.3*** 20 (2) 16-25 2.1* 26 (16) 0-75 3.09***
Sunflower 3 (5) 0-10 2.06* 17 (1) 15-21 16.3*** 70 (9) 43-90 1.88*
Honeydew 6 (6) <0.5-27.3 10.18*** 17 (1) 15-22 4.1*** 90 (12) 57-118 15.8***
Polyfloral 4.5 (4.8) <0.5-39.8 9.08*** 18 (2) 15-24 4.5*** 64.(19) 7-116 3.6***
* p<0.05; ***p<0.001
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
60
4. Conclusion
Consideration of the botanical origin of the eleven types of honey analysed permitted
grouping according to the physicochemical parameters. The clearest honeys were at the
same time the ones with the highest moisture while the darkest honeys were found to
have the lowest moisture levels. Colour was the parameter most affected by the type of
honey and year of harvesting, whereas HMF was the least affected in both cases. There
were some unacceptable outliers from specific beekeepers which exceeded the
permitted values for moisture and the recommended values of HMF. This indicates the
important role that the beekeeper has in attaining raw honey with the correct
physicochemical parameters, and even the characteristic colour which the market
requires. When the process is controlled carefully, the key to the quality of the end
product lies in the good quality of the raw material. Therefore, adequate training in good
beekeeping practices is vital to obtain the product that the consumer expects and
legislation requires.
5. References
BOE 24621. (2002) Reglamento de la Denominación de Origen Protegida "Miel de
Granada" y de su Consejo Regulador. 44261-44268.
Bogdanov S. (2002). Harmonized methods of the International Honey Commission. Bern,
Switzerland: Swiss Bee Research Centre, FAM, Liebefeld, CH 3003.
Codex Alimentarius. (2001). Revised Codex Standard for Honey. Codex Stan 12-1981.
Codex Alimentarius Commission. Rev. 1(1987). Rev. 2.
Council Directive 2001/110 (2002) relating to honey. Official Journal of the European
Communities. L10, 47-52.
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
61
DOGV No. 4167. (2002). Reglamento de la Marca de Calidad CV para miel de azahar y
romero in Diario Oficial de la Generalitat Valenciana. Área de publicaciones de la
Presidencia de la Generalitat Publishing, Valencia (Spain).
Escriche I., Kadar M., Domenech E. & Gil-Sánchez L. (2012).A potentiometric electronic
tongue for the discrimination of honey according to the botanical origin.
Comparison with traditional methodologies: Physicochemical parameters and
volatile profile.J. Food Eng. 109, 449-456.
Gómez-Pajuelo A. (2004). Origen botánico de la miel, in Montagud (Eds.), Mieles de
España y Portugal. Barcelona, Spain, pp. 50-56.
Panseri S., Manzo A., Chiesa L.M. & Giorgi, A. (2013). Melissopalynological and Volatile
Compounds Analysis of Buckwheat Honey from Different Geographical Origins and
Their Role in Botanical Determination. Journal of Chemistry, Volume 2013, Article
ID 904202, 11 pages.
Persano-Oddo L. & Bogdanov S. (2004). Determination of honey botanical origin:
Problems and issues. Apidologie. 35, 2-3.
Persano-Oddo L. & Piro R. (2004). Main European unifloral honeys: Descriptive sheets.
Apidologie. 35, 38-81.
Persano-Oddo L., Heard T., Rodríguez-Malaver A., Pérez R.A, Fernández-Muiño M.A.,
Sancho M.T., Von der Ohe W., Sesta G., Lusco L. & Vit P. (2008). Composition and
antioxidant activity of Trigona carbonaria honey from Australia. J. Med. Food, 11,
789-794.
Piazza M. G. & Persano-Oddo L. (2004). Bibliographical review of the main European
unifloral honeys. Apidologie. 35(1), 94-111.
Preys S., Vigneau E., Mazerolles G., Cheynier V. & Bertrand D. (2007). Multivariate
prototype approach for authentication of food products. Chemometr Intell Lab. 87,
200-207.
Saenz C. & Gómez C. (2000). Mieles españolas: Características e identificación mediante
el análisis del polen, ed. Mundi-Prensa, España.
Soria A.C., González M., de Lorenzo C., Martınez-Castro I. & Sanz, J. (2004).
Characterization of artisanal honeys from Madrid (Central Spain) on the basis of
their melissopalynological, physicochemical and volatile composition data. Food
Chem. 85, 121-130.
Tornuka F., Karamanb S., Ozturk, I., Toker O. S., Tastemurb B., Sagdica O., Doganb M. &
Kayacierb A. (2013). Quality characterization of artisanal and retail Turkish blossom
honeys: Determination of physicochemical, microbiological, bioactive properties
and aroma profile. Ind Crop Prod., 46, 124-131.
Physicochemical quality parameters at the reception stage of the honey packaging process: Influence of type of honey, year of harvest and beekeeper
62
Turhana I., Tetika N., Karhana M., Gurelb F. & Tavukcuoglua, H.R. (2008). Quality of
honeys influenced by thermal treatment. LWT - Food Sci. Technol. 41, 1396-1399.
Visquert M., Vargas M. & Escriche I. (2014). Effect of postharvest storage conditions on
the colour and freshness parameters of raw honey. Inter J. Food Sci. and Tech., 49,
181-187.
Von Der Ohe W., Persano-Oddo L., Piana M. L., Morlot M. & Martin, P. (2004).
Harmonized methods of melissopalynology. Apidologie, 35
Marisol Juan-Borrás, Angela Periche, Eva Domenech, Isabel Escriche
International Journal of Food Science & Technology, 50 (7),
1690–1696. (2015)
3.2. Correlation between methyl anthranilate level and percentage of
pollen in Spanish citrus honey
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
65
Abstract
The common level of methyl anthranilate (MA) in Spanish citrus honey and the
correlation between this compound and the percentage of citrus pollen (sometimes
underrepresented) is evaluated. The MA analysis methodology was validated before
analyzing the honeys (harvested in 2011 and 2012), which were characterized by pollen,
MA, hydroxymethylfurfural, electrical conductivity, moisture and colour. Pollen ranged 1-
88% and MA 0.5-5.9 mg/kg and there was no quantitative correlation between both.
However, significant correlations with moderate Pearson coefficients were observed:
MA/electrical conductivity (-0.678); MA/colour (-0.559); pollen/electrical conductivity (-
0.553) and pollen/colour (-0.556). 89.2 % of samples from 2011 and 95.4% from 2012
had the required level of citrus pollen (at least 10%), although only 53.5% and 61.4%,
respectively, had the commercially required of MA (2 mg/kg,). Only about half of the
samples satisfied both parameters. The MA value should be recommended only when
the honey has an unexpectedly low percentage of citrus pollen, and after assessing
organoleptic and physicochemical parameters.
Keywords: citrus honey, methyl anthranilate, Citrus spp. pollen, correlation, validation,
melissopalynological analysis, electrical conductivity, colour.
1. Introduction
Traditionally, honey is authenticated by identifying and quantifying the percentage of
pollen (Bogdanov, 2002). However, in the case of citrus varieties this melissopalynological
analysis alone, widely used for other types of monofloral honey, is sometimes not
sufficient. The problem lies in the fact that the production of pollen and nectar in citrus
blossom is not always simultaneous. Which is to say that citrus trees sometimes produce
nectar before the anther produces pollen. Apart from this, bees occasionally collect
nectar from sterile hybrid varieties of citrus trees, which are characterized by their small
amounts of pollen. For these reasons the percentage of pollen in citrus honey may be
lower than expected (Molins, et al., 1995). This clearly implies a difficulty in relation to
the minimum percentage of pollen from Citrus spp. required for this type of honey: 10%
(Persano-Oddo, et al., 1995; Molins et al., 1995; DOGV, 2002); 12% (Gómez-Pajuelo,
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
66
2004); 18.6% (Persano-Oddo & Piro, 2004); ≥5 % (Rodriguez et al., 2010). This is a
problem for the commercialization of this valuable type of honey since it can be unfairly
rejected, even though its organoleptic characteristics are undisputed.
Methyl anthranilate (MA) is a specific compound in citrus blossom nectar; its aroma is
characteristic of this type of flower (ISO 5496, 2006) which may vary depending on the
citrus cultivars (Jabalpurwala, et al., 2009). The presence of this compound in a honey
indicates that the bees have taken nectar from citrus trees (White, 1966; White & Bryant,
1996; Castro-Vazquez, et al., 2007). For this reason, MA has been used for decades as a
marker in citrus honey and should be a good tool for the classification of citrus honey
when the level of pollen is very low. However, at present, commercial transactions
require that citrus honey has a minimum citrus pollen content (between 10 and 20%),
together with a methyl anthranilate level of at least 2 mg/kg (Sesta, et al., 2008).
According to the data reported by different authors, the quantity of MA in the nectar
citrus varies, depending on the country. Citrus honey from Florida has a mean value of
3.10 mg/kg of MA (SD = 0.91) (White & Bryant, 1996). However, Sesta et al., in 2008
suggested that a minimum content of 0.5 mg/kg is sufficient for Citrus honey produced
in Italy. There are a few old studies related to the level of this aromatic compound in
Spanish citrus honey: minimum of 0.5 mg/kg (Serra-Bonvehí, 1988), and average of 2.3
mg/kg (SD=0.5) (Ferreres, et al., 1994; Serra-Bonvehí & Ventura, 1995).In addition to
variation due to geographic origin, levels of MA can differ depending on storage
conditions (Serra-Bonvehí & Ventura, 1995; White & Bryant, 1996; Sesta et al, 2008).
Therefore, it is important to take this into consideration in order to make appropriate
comparisons.
Clearly, there is a great disagreement about the required level of MA in citrus honey.
However, the origin of this discrepancy could in part be in the application of different
analytical techniques over the last three decades: HPLC-DAD (Ferreres, et al., 1994; Nozal,
et al., 2001; Sesta et al, 2008), Photometry (White & Bryant, 1996),HS-SPME-GC (Bertelli,
et al., 2008; Papotti, et al., 2009; Papotti, et al., 2012), P&T/GC-MS-thermal desorption
(Escriche, et al., 2011). As there is no official methodology described for the analysis of
MA, the only way to ensure the quality of the obtained results is to validate the
quantification methodology.
For the above mentioned reasons, the present work aims to evaluate the level of MA in
Spanish citrus honey and to determine the extent to which this level can be related to the
percentage of pollen from citrus genus. In order to ensure the utility of the
chromatographic procedure used to quantify this compound, it was validated before
analyzing the samples.
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
67
2. Materials and Methods
2.1.-Honey samples
Ninety eight different samples of Spanish citrus honey were used in this study. The
samples were harvested in the only areas (East and South) of Spain where citrus trees
grow. The same beekeepers (B) directly supplied twenty eight samples from 2011 and
fifty samples in 2012, ensuring that the hives had been located in citrus groves. The
remaining twenty samples (10 from 2011 and 10 from 2012) were purchased, in the same
period of time, in different retail outlets (R) in the city of Valencia, checking in all cases
that they were sold as citrus honey harvested in Spain. A melissopalynological analysis
was carried out on all samples to ascertain the percentage of pollen of Citrus spp.
All samples were analysed as soon as they were received in the laboratory. Samples that
came from beekeepers were sent to the laboratory immediately after they were
harvested. None of the samples used exhibited signs of crystallization.
2.2.-Melissopalynological analysis
Pollen analysis was performed using the recommendations of the International
Commission for Bee Botany (Von der Ohe, et al., 2004), without an acetolysis solution to
preserve all the components. Slides were prepared as follows: 10 g of honey were
dissolved in acidulated water (H2SO4, 5%) on a heating plate at 40 ºC. Subsequently, it
was centrifuged, the supernatant was decanted and the precipitate was suspended in 10
mL of distilled water. A second centrifugation was performed, the supernatant was
decanted off and 0.2 mL of water was added to the precipitate. After stirring, 0.2 mL were
deposited on a slide and dried. Finally a drop of glycerin was used to seal the coverslip. A
light microscope (Zeiss Axio Imager, Göttingen, Germany) with DpxView LE image analysis
software attached to a DeltaPix digital camera was used in this analysis. A count of at
least 600 pollen grains was performed observing at ×400–1000 magnifications. These
grains of pollen were classified according to pollen morphology as in the literature
(Carretero, 1989; Saenz & Gómez, 2000).
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
68
2.3.-Methyl anthranilate analysis
2.3.1. Methodology
A HPLC-DAD (Diode-Array Detection) method, based on Sesta et al. (2008), was used in
the present work for MA determination. The method consists of acid hydrolysis, followed
by extraction with copolymer cartridges and then chromatographic analysis. An LC Agilent
1120 Compact LC, including a binary pump, a thermostat column compartment, an auto-
sampler and a UV detector were used. The chromatographic column and the software
system used in the HPLC-UV method was the same as that used for the HMF analysis.
Chromatographic separation was carried out with a mobile phase consisting of water (A)
and acetonitrile (B). Binary gradient conditions were used: first an isocratic step from 0
to 3.1 min with 70% A, and then a linear gradient was applied arriving at 42% A in 2 min.
After that, a second linear gradient was applied arriving at 10% A, held for 2 min, and re-
equilibrates to the initial conditions in 3 min. The flow-rate was 1 mL/min. The injection
volume was 20 L, and the oven column was maintained at 30 ºC. The MA was monitored
at 335 nm.
A HPLC Alliance 2695, with a 2996 photodiode array detector (Waters, USA), equipped
with the same column, was also used to corroborate the absorbance spectra, necessary
for the identification of MA. The UV absorbance spectrum of MA presented an intense
absorbance peak at 218 nm and 2 less intense peaks at 245 and 334 nm. As noted by
Sesta et al. (2008), it was considered more appropriate to quantify at the absorbance of
334 nm as it presents less interference than the other ones.
Under the specified chromatographic conditions the MA peak was eluted at a retention
time of about 6.8 min. Quantification was realized by means of matrix calibration curves
obtained from spiked fortified blank samples. In order to ensure the quality of the results
and evaluate the stability of the proposed method, an internal quality control (a spiked
blank sample with a final concentration of 2 mg/kg) was injected in the equipment as a
first step before each batch of sample.
In all cases a polyfloral honey with absence of MA was used as a honey blank. The absence
of citrus pollen in this honey was corroborated previously.
Reagents and standards solutions
HPLC-grade acetonitrile was purchased from VWR and the standard MA (purity > 99%)
from Merck (Darmstadt, Germany). Analytical grade sulphuric acid was from Scharlab
(Barcelona, Spain). For Solid Phase Extraction, Oasis HLB cartridges (200 mg/6 mL) from
Waters were used. De-ionized water of MilliQ quality was used throughout.
The stock standard solution of MA was prepared by weighing the appropriate amount of
the pure standard and diluting it with water to obtain a final concentration of 1 mg/mL.
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
69
The working standard solution was obtained at a concentration of 0.1 mg/mL in H2SO4
1M in the same way as the samples. The stock standard solution was stored at -20ºC and
the working standard solution was at +4ºC.
Validation of the MA analysis method
The guidelines established by Commission Decision (2002), were followed in order to
validate the MA analytical methodology. To this end several parameters were studied:
linearity, accuracy and precision (repeatability and reproducibility). The accuracy of the
method was established through recovery studies and the precision by: repeatability or
intraday precision (RSDr) and reproducibility or interday precision (RSDR). LODs (limit of
detection) and LOQs (limit of quantification) were estimated as the amount of analyte for
which signal-to-noise ratios (S/N) were higher than 3 and 10 respectively.
2.4.-Physicochemical and colour analysis
5-hydroxymethylfurfural (HMF) determined by HPLC-UV methodology and
physicochemical parameters (moisture content and electrical conductivity) were
analyzed as described in “Harmonized Methods of the European Honey Commission”
(Bogdanov, 2002). The chromatographic column used for the analysis of HMF was a
ZORBAX Eclipse Plus C18 (4.6 x 150 mm, 5 m particle size) purchased from Agilent
(Agilent Technologies, USA). The mobile phase for this analysis was water-methanol
(90:10, v:v), with a flow rate of 1 mL min-1. The detector was set to 285 nm. The EZChrom
Elite system software was used for HPLC data processing. Colour was determined with a
millimeter Pfund scale C 221 Honey Color Analyzer (Hanna Instruments, Spain). All
analyses were performed in triplicate.
2.5.-Statistical analysis
The pollen percentage, physicochemical (MA, HMF, electrical conductivity) and colour
data were analyzed by a multifactor analysis of variance (ANOVA) (significance level α =
0.05) (using Statgraphics Centurion for Windows) to study the influence of the year of
harvesting and the type of sample (beekeeper and retail).The method used for multiple
comparisons was the LSD test (least significant difference) with a significance level α =
0.05. The bivariate Pearson correlations were obtained (significance level α = 0.05) to
measure the strength and direction of the linear relationships between pairs of variables
using SPSS 16.0. The contingency table analysis (cross tabulations) was carried out to
evaluate the interrelation between pollen and MA, considering these variables as
categorical, using the same SPSS 16.0 (IBM).
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
70
3. Results and Discussion
3.1.-Validation of methyl anthranilate analytical
methodology
The results from the validation procedure are shown in Table 1.In order to obtain the
linearity evaluation an external standard calibration curve was constructed using spiked
fortified blank honey (honey without MA) with final concentration levels of: 0.5, 1, 2, 3
and 5 mg of MA/kg honey. These concentrations covered the values of this compound
which were expected to be found in the honey samples. Six replicates were carried out
for each level. Injections were performed in triplicate. A calibration curve was obtained
by plotting the peak area of the compound at each level versus the concentration of MA
added to the sample. A good linearity response in the range of the concentration
considered was observed, with a correlation coefficient (R2=0.995) between peak areas
and injected nominal concentrations.
The recovery studies were performed by adding known quantities of MA to the blank
honey (0.5, 1, 2, 3 and 5 mg of MA/kg). All spiked fortified sample levels were done in
triplicate and analyzed by the HPLC method. The results displayed in Table 1 show that
the method used led to recovery of MA between 96 and 105% for the concentration
range studied. The relative standard deviation (RSD) corresponding to recovery values
ranged from 6.0 to 11.6%. As these values were less than 20%, the accuracy of the
analytical method was confirmed (Commission Decision, 2002).
Repeatability (RSDr) was evaluated by performing the assay on six replicates of fortified
honey samples, at the same levels (0.5, 1, 2, 3 and 5 mg of MA/kg), and performed by the
same operator on the same day. To evaluate reproducibility (RSDR) the experiment was
carried out on 3 consecutive days, with 2 different operators. The results were expressed
as the percentage of relative standard deviation of the measurements.
As shown in Table 1, intra-day precision (RSDr) ranged from 1.40% to 7.20% and inter-day
precision (RSDR) from 4.50% to 13.96%. These RSD values are in complete agreement
with Commission Decision (2002), requirements since they were always lower than 20%
for all the concentration levels assayed. Therefore it can be concluded that the method
used has good precision. The LOQ obtained was 0.1 mg of MA/kg. The results of the
validation demonstrate that the applied analytical procedure guarantees satisfactory the
quantitative values of MA obtained in the samples analyzed.
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
71
Table 1. Validation parameters (accuracy and precision) of methyl anthranilate (MA) methodology. The numbers in brackets are the relative standard deviation. Six replicates were carried out for each level.
Intra-day-precision Inter-day-precision
MA Added
(mg/kg)
Mean Recovery
RSD (%)
Mean value (mg kg-1)
RSDr (%)
Mean value (mg kg-1)
RSDR (%)
0.5 96.0 (6.0) 0.50 (4.10) 0.49 (12.00)
1.0 105.0 (6.5) 1.08 (7.20) 1.03 (5. 80)
2.0 104.0 (10.8) 2.10 (4.11) 2.06 (13.96)
3.0 99.0 (11.6) 2.63 (4.02) 2.36 (13.70)
5.0 100.0 (6.4) 5.06 (1.40) 5.05 (4.50)
3.2.-Melissopalynological, physicochemical, colour and
methyl anthranilate characterization
Table 2 shows the percentage of Citrus spp. pollen, the average values of MA and the
physicochemical parameters quantified (HMF, electrical conductivity, and moisture), as
well as the colour of each of the samples supplied by beekeepers (B) and purchased in
retail outlets (R) in 2011 and in 2012. In addition, this table shows the result (P-value, F-
ratio and minimum and maximum LSD values) of the multifactor ANOVA carried out
considering the factors: year of collection (2011 and 2012) and “type of sample” of citrus
honey (beekeepers and retail). The respective double interactions were not significant in
any case (data not shown). Figure 1 shows the box and whisker plots for all the values
obtained in order to facilitate the comparison of variability patterns between the four
sources of citrus honey (beekeepers 2011; beekeepers 2012; retail 2011 and retail 2012).
The citrus pollen percentage varied significantly among type of samples (beekeepers and
retail) but not among years of harvesting. This percentage ranged from 1 to 88
(average=34) and from 1 to 69 (average=33) in beekeeper samples from 2011 and 2012,
respectively. With regard to the supermarket samples, the pollen percentage ranged
from 7 to 40 (average=18) and from 8 to 42 (average=19), respectively.
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
72
Table 2.-Percentage of Citrus spp. pollen, average values (n = 3) of methyl anthranilate (MA) and physicochemical parameters (HMF, electrical conductivity, moisture) and the colour of each of the samples supplied by beekeepers (B) (28 from 2011 and 50 from 2012) and purchased in retail outlets (R) (10 from 2011 and 10 from 2012). Average and standard deviation (in brackets) for each parameter. Multifactor ANOVA results (P-value, F-ratio, minimum and maximum LSD values) obtained for the factors: year (2011 and 2012) and sample (beekeepers and retail).
Pollen
(% Citrus spp.)
MA
(mg/kg)
HMF
(mg/kg)
Electrical
conductivity
(S cm-1)
Moisture
(mg/100g)
Colour
(mm Pfund)
Samples 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012
B01 48 35 1.9 1.4 5.1 1.2 264 167 15.4 15.4 36 2
B02 42 48 3.2 2.5 1.1 0.5 183 150 15.5 16.7 28 3
B03 73 47 2.1 3.6 4.0 1.4 241 161 16.2 14.5 37 5
B04 48 40 1.2 3.3 3.7 1.7 243 157 17.9 17.8 27 6
B05 36 68 1.3 3.5 1.7 1.6 167 163 15.5 17.2 17 6
B06 1 47 0.5 4.5 11.8 1.6 283 164 16.8 16.4 37 7
B07 5 46 1.3 3.3 7.6 1.5 248 167 16.2 18.0 32 9
B08 30 69 0.9 3.5 9.4 2.0 271 169 15.8 17.4 43 11
B09 27 4 1.5 4.7 3.8 1.5 208 187 16.3 14.5 35 11
B10 63 48 3.8 5.9 1.7 1.1 167 161 18.6 18.2 20 11
B11 57 60 4.0 2.5 1.5 2.6 172 170 17.8 16.3 20 11
B12 33 37 2.2 1.1 1.5 0.7 194 233 17.2 15.9 22 19
B13 42 37 2.2 2.9 2.4 4.3 198 183 16.7 18.0 26 13
B14 46 23 2.5 2.2 3.2 2.6 189 210 16.6 14.3 19 18
B15 12 48 1.9 2.9 16.5 4.6 205 188 16.6 16.9 33 14
B16 36 1 2.1 3.6 7.9 2.2 202 254 16.5 14.8 25 16
B17 43 37 2.2 2.6 5.4 3.8 185 207 17.3 14.0 22 18
B18 88 30 1.3 1.6 25.0 3.0 212 290 16.3 13.2 35 20
B19 53 65 3.8 2.4 3.8 3.9 208 233 15.4 16.1 24 20
B20 8 12 1.1 1.5 5.6 7.1 215 238 16.0 17.1 33 21
B21 19 42 1.9 2.3 8.7 4.5 210 246 14.8 23.3 39 21
B22 11 35 2.9 2.3 7.6 5.2 228 222 15.8 16.7 37 22
B23 35 10 2.1 1.9 6.8 3.9 209 288 13.8 13.3 37 23
B24 17 20 1.9 2.9 4.6 4.9 266 239 16.6 14.5 40 24
B25 10 45 2.4 1.6 0.7 4.2 169 259 17.8 13.3 20 24
B26 42 26 1.9 1.7 4.9 2.8 233 273 18.6 14.7 32 25
B27 14 20 2.2 2.7 4.8 7.9 255 258 15.4 16.8 40 27
B28 12 28 2.7 0.8 0.9 5.3 220 312 15.6 14.4 37 27
B29 30 2.5 11.1 241 17.2 27
B30 19 1.4 4.1 332 13.3 29
B31 27 2.0 5.2 273 13.8 29
B32 34 1.3 4.3 278 13.6 29
B33 32 3.0 4.1 278 16.4 30
B34 12 1.9 7.2 315 16.5 32
B35 36 1.0 4.3 313 14.9 33
B36 31 1.1 6.0 288 16.9 33
B37 15 2.6 9.7 242 17.2 34
B38 16 1.1 2.9 381 13.1 36
B39 24 2.6 7.8 236 17.4 36
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
73
Table 2 (cont.)
Pollen
(% Citrus spp)
MA
(mg/kg)
HMF
(mg/kg)
Electrical
conductivity
(S cm-1)
Moisture
(mg/100g)
Colour
(mm Pfund)
Samples 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012
B40 16 2.4 9.5 224 18.1 36
B41 17 1.7 4.5 357 16.4 37
B42 15 1.4 5.8 373 15.9 40
B43 13 2.5 3.1 217 16.0 38
B44 30 1.2 10.8 284 17.1 40
B45 35 1.4 1.2 167 15.4 2
B46 48 2.5 0 150 16.7 3
B47 47 3.6 1.4 161 14.5 5
B48 40 3.3 1.7 157 17.8 6
B49 68 3.5 1.6 163 17.2 6
B50 47 4.5 1.6 164 16.4 7
Average 34(21) 33(17) 2.1(0.9) 2.4 (1.8) 5.8(5.2) 4.1(2.6) 217(7) 230 (63) 16.3(0.2) 16.0(1.7) 31(2) 20(11)
R01 15 8 0.7 2.0 34.3 25.2 175 240 16.6 16.6 50 44
R02 14 42 1.5 1.9 17.0 33.1 255 220 17.9 16.4 39 50
R03 8 9 0.8 1.8 32.0 32.2 171 210 16.3 18.7 47 26
R04 25 11 0.6 0.7 17.7 25.3 208 229 18.3 18.3 58 39
R05 21 28 1.7 1.7 21.8 30.4 320 193 16.3 16.4 58 26
R06 9 26 2.9 1.5 33.8 14.1 191 245 16.2 16.5 37 45
R07 10 11 1.2 1.4 24.3 16.0 240 244 16.9 16.3 37 40
R08 7 22 2.4 1.0 34.3 17.1 238 198 17.5 16.2 48 28
R09 26 16 1.4 2.9 42.4 22.0 228 235 15.2 17.0 51 35
R10 40 16 1.9 0.8 24.8 33.1 229 234 15.6 16.9 37 36
Average 18 (10) 19(11) 1.5(0.7) 1.6(0.7) 28.2(8.1) 25.2(7.5)) 228 (12) 224 (19) 16.5(0.3) 16.9(0.8) 46(3) 37(8)
Year factor
P-value F-ratio LSD-2011 (min/max) LSD-2012 (min/max)
0.679 0.17
22.50/31.26
19.08/31.51
0.038 4.41
1.60/2.12
1.97/2.71
0.518 0.42
13.85/16.78
12.40/16.57
0.353 0.87
202.11/239.31
215.33/249.95
0.846 0.04
16.02/16.78
15.96/16.96
0.006 7.72
33.06/40.61
26.01/33.04
Sample factor
P-value F-ratio LSD-B
(min/max) LSD-R
(min/max)
0.0004 13.01
29.38/36.65
12.48/25.84
0.203 1.64
2.03/2.46
1.56/2.35
0.0000 242.52
3.64/6.08
22.70/27.19
0.605 0.27
217.45/242.53
201.25/245.46
0.079 3.13
15.85/16.45
16.16/17.26
0.0000 25.24
24.12/29.21
35.21/44.19
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
74
Figure 1.- Box and whisker plots for pollen, MA, HMF, electrical conductivity, moisture and colour for honeys from the bee-keepers (B) and retail outlets (R). Samples harvested in 2011 and in 2012.
Similarly, for MA the biggest dispersion between maximum and minimum corresponds to
beekeeper samples (0.5-4.0 mg/kg in 2011 and 0.8-5.9 mg/kg in 2012) with means of 2.1
and 2.4 mg/kg, respectively; where as the retail samples ranged from 0.6 to 2.9 mg/kg
(mean= 1.5 mg/kg) for 2011 and from 0.7 to 2.9 mg/kg (mean= 1.6 mg/kg) for 2012. In
general, although not significant differences were observed for MA related to the type of
sample, the lowest values and the lowest dispersion for MA (the same as for % of pollen)
were detected, as expected, in retail samples. This is because the honey packaging
industry mixes the raw honeys to produce relatively homogeneous batches to meet the
requirements and specifications that companies have for each type of monofloral honey
such as citrus honey. After reception, these industries analyse the physicochemical
properties and pollen of the raw honey in order to discern the characteristics of the raw
batches and be able to mix them appropriately.
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
75
The HMF of the beekeeper honeys showed relatively low average values (5.8mg/kg for
2011 and 4.1mg/kg for 2012). However, unexpectedly high values of 16.5 and 25 mg/kg
were observed in 2011, probably due to sporadic bad practices. Such high values were
not observed in 2012 in any case. As commercialized honeys are usually thermally treated
(liquefied and pasteurized) by the industry, the highest values were usually and
unsurprisingly found in the retail honeys. It should to be pointed out that one sample
exceeded the overall permitted limit of 40 mg/kg for HMF (Council Directive 2001/110
relating to honey, 2002).
Electrical conductivity was quite low in the majority of the samples, as expected for the
type of honeys under consideration (Persano-Oddo & Piro, 2004; Bogdanov, et al., 2004).
The average values were very similar in both types of samples (217 and 230 Scm-1 in the
beekeeper samples, and 228 and 224 Scm-1 in the retail ones) without significant
differences neither year nor type of sample.
In the same way no significant differences were found for moisture in relation to both
factors, and maximum values did not exceed 19.0 g/100g (Cano, et al., 2001), with one
exception of 23.3 g/100g (in beekeeper samples of 2012). Beekeeper samples exhibited
lower minimum values, reaching 13.1 g/100g, whereas the minimum value for retail
samples was 15.2 g/100g. Being spring honeys, higher moisture values than those
observed could be expected (Serra-Bonvehí, 1988). Again, a lesser dispersion of moisture
values, reflecting greater homogeneity, can be seen for retail honey due to processing
practices, as mentioned previously.
On the contrary, with regard to colour, significant differences were found both for year
and supplier factors. Beekeeper samples had lower values, especially those from 2012.
Retail samples reached values of up to 58 mm Pfund. These higher values could be mainly
due to the influence of industrial thermal treatments (liquefaction and pasteurization)
(Visquert et al., 2014). Although colour level is not a requirement for citrus honey
commercialization, if this honey were sold with a specific quality mark, then particular
colour requirements would have to be met. This is the case of the Valencian Quality mark
(DOGV, 2002) which requires a maximum of 30 mm on the Pfund scale to benefit from
this Mark. Considering this level, all of the retail samples and more than half of those
from beekeepers in 2011 were above it. However, in the case of beekeeper samples from
2012, more than 75% had values lower than 30 mm on the Pfund scale.
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
76
3.3.-Relationship between the analysed parameters In order to ascertain the possible linear dependence between the analysed variables,
Pearson correlation coefficients were calculated for each pair of variables. Only the
beekeeper samples were considered in this correlation since the lack of freshness of the
retail samples (high HMF values) could have influenced the correlated variables (Serra-
Bonvehí, 1988; Sesta et al., 2008).
Table 3 shows the correlation matrix obtained; the number in brackets is the P-value
which tests the statistical significance of the estimated correlations at the 95.0%
confidence level. Although some of the correlations are significant since P-values are
below 0.05, the strength of the linear relationship between each pair of variables is far
from the value +1 or -1. The best correlations are shown for colour and HMF (0.674 for
2011 and 0.706 for 2012) and for colour and electrical conductivity (0.596 for 2011 and
0.812 for 2012). The observed correlation between colour and HMF is coherent
considering that since from harvesting, honey tends to increase HMF and colour naturally
as a result of Maillard reactions (Sancho, et al., 1992). The correlation between colour
and electrical conductivity is widely accepted. In general terms, the darker the honey the
higher electrical conductivity. Since the samples considered for this correlation were only
the unprocessed honeys, the mineral content was the main cause of this relationship
(Bogdanov et al., 2004).
Previous works considered that the MA value could be related to the percentage of pollen
and to other specific physicochemical parameters such as moisture and HMF (Serra-
Bonvehí, 1988). These authors suggest that MA content decreases with the loss of
freshness, with one year old samples showing a lower level of this compound than fresh
samples.
However no good linear relationship between MA and HMF (-0.375 for 2011 and -0.336
for 2012) was observed in this present work. Similarly, there was also no correlation
between MA and moisture, despite claims by Serra-Bonvehí (1988) that high moisture
content can cause aromatic losses in honey to the point of reducing MA content. It is
important to highlight that the range of moistures found in this work was too narrow to
draw conclusions about the influence of moisture on this parameter.
In relation to the other physicochemical parameters (electrical conductivity and colour),
significant correlations, with moderate Pearson coefficients were observed between
them and MA, and pollen, especially for 2012 samples, with values of: MA/electrical
conductivity=-0.678; MA/colour=-0.559; pollen/electrical conductivity=-0.553 and
pollen/colour=-0.556. However, on the contrary to what was expected, there was no
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
Table 3. Correlation matrix (Pearson correlation coefficients) between percentage of pollen (Citrus spp.), methyl anthranilate (MA), HMF, moisture, electrical conductivity and colour. Samples harvested in 2011 and 2012.
Pollen MA HMF Moisture Electrical conductivity
2011 2012 2011 2012 2011 2012 2011 2012 2011 2012
MA (mg/kg) 0.347
(0.062)
0.253
(0.098)
HMF(mg/kg) -0.292
(0.011)
-0.380
(0.011)
-0.375
(0.001)
-0.336
(0.026)
Moisture (g/100g) 0.090 0.285 0.037 0.303 -0.069 0.200
(0.437) (0.061) (0.754) (0.046) (0.556) (0.194)
Electrical conductivity
(S/cm-1)
-0.213
(0.065)
-0.553
(0.000)
-0.334
(0.003)
-0.678
(0.000)
0.101
(0.386)
0.413
(0.005)
-0.132
(0.255)
-0.352
(0.019)
Pfund colour (mm) -0.401
(0.003)
-0.556
(0.000)
-0.519
(0.000)
-0.559
(0.000)
0.674
(0.000)
0.706
(0.000)
-0.189
(0.102)
-0.107
(0.491)
0.596
(0.000)
0.812
(0.000)
Numbers in brackets = P-value
77
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
78
correlation between MA and the percentage of pollen, the coefficient being 0.347 and
0.253 for samples from 2011 and 2012, respectively.
Once the limited quantitative correlation between MA and the percentage of pollen was
demonstrated, it seemed interesting to try to correlate them from a qualitative point of
view. Therefore both variables were now considered to be categorical. For this purpose,
the samples were classified according to whether they fulfilled the criteria for minimum
level of pollen [Citrus spp. pollen higher than 10%: Molins et al., 1995; Persano-Oddo, et
al, 1995; DOGV, 2002] and minimum level of MA [2 mg/kg: Persano-Oddo & Piro, 2004;
Sesta et al., 2008 and commercial criteria according to the Spanish industry]. As a
consequence, a contingency table was made (only the beekeeper samples were
considered since they were always raw samples and not mixed) (Table 4), which is a
double entry constructed by listing the variable “pollen” as rows and the variable “MA”
as columns. Each variable has only two levels: comply or not comply. Each cell in the table
represents the percentage of samples that satisfy the criterion of the row (% of Citrus spp
pollen) or the column (MA concentration), both (% pollen and MA) or neither. Of all the
observations, 89.2% (in 2011) and 95.4% (in 2012) comply with the pollen requirement
(at least 10% citrus pollen) for a Mark of Quality (e.g. the Valencian Quality Mark: DOGV,
2002). In the case of methyl anthranilate the percentage of compliance (at least 2 mg/kg)
was 53.5 and 61.4%, respectively. As mentioned above, in the case of citrus varieties, in
addition to pollen, the methyl anthranilate content is required for commercial
transactions, as was suggested several years ago by some European laboratories (Sesta
et al., 2008).
Table 4.-Contingency table for pollen (comply with 10% of Citrus spp. pollen) and MA (comply
with 2 mg/kg).Samples harvested in 2011 and 2012.
Comply with
MA2mg/kg
Not comply
MA2mg/kg
Total
2011 2012 2011 2012 2011 2012
Comply with pollen
10% Citrus spp 53.5% 56.8% 35.7% 38.6% 89.2% 95.4%
Not comply with pollen
10% Citrus spp 0% 4.6% 10.8% 0% 10.8% 4.6%
Total 53.5% 61.4% 46.5% 38.6% 100.0% 100.0%
In this work, 53.5% for 2011 and 56.8% for 2012 of the samples fulfilled both % pollen
and MA concentration. On the other hand, 35.7 % and 38.6 % of the samples met % pollen
but not MA, and 4.6 % of the samples from 2012 complied with MA but not % pollen.
Finally, 10.8% of samples from 2011 met neither % pollen nor MA.
It does not seem logical that MA, a parameter that has been proposed to complement
the information given by pollen in citrus honeys when pollen is under-represented, is
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
79
actually an impediment to its classification. The way that MA is being applied does not
seem to help this purpose. In fact, if melissopalynological analysis alone were considered
in this study, as in other types of monofloral honey, 89.2 % of samples from 2011 and
95.4% from 2012 would be accepted. However, 35.7 % and 38.6% of the samples,
respectively, which complied with the pollen requirement would be rejected
commercially for not reaching 2 mg/kg for MA.
According to the results obtained, the criterion of 2 mg/kg for MA seems to be too
demanding, and therefore not suitable for Spanish citrus honeys. Studies carried out by
other authors on this type of honey from Spain and Italy (Sesta et al., 2008; Papotti et al.,
2009), concluded that MA content was usually lower than the commercial requirement
of 2 mg/kg. This fact was demonstrated even in honeys, which obviously had the sensory
characteristics of this type of honey. Maybe, for these Mediterranean countries it would
be appropriate to propose a lower value than is expected in other parts of the world
(White & Bryant, 1996). According to the results, it seems more appropriate to only
demand this value in the case of honeys with an unexpectedly low percentage of citrus
pollen. That is, honeys with the typical physicochemical and sensory characteristics of
citrus honey (light amber colour, evident notes of acidity and a very unique flavour due
to the presence of specific aromatic substances) but without sufficient citrus pollen for
this type of honey (Escriche, et al., 2011).
6. Conclusion
The results showed that there was a very weak linear correlation between the level of
methyl anthranilate and the percentage of pollen in the samples analysed. In almost half
of the cases the quantity of MA in Spanish citrus honey was lower than the commercial
requirement, which was also reported by other authors for Italian citrus honey. This
occurs even though the honeys have a more than sufficient commercial level of citrus
pollen. This paper proposes reconsidering the level of MA required in Spanish citrus
honey and applying a more realistic value. But above all, to only take this parameter into
account in the case of honeys with a surprisingly low percentage of citrus pollen, and
after evaluating their organoleptic and physicochemical properties.
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
80
Acknowledgment
This study forms part of a project funded by the company Melazahar Cooperativa
Valenciana (Spain), with the title of "Idoneidad del metilantranilato en la clasificación de
mieles de azahar (UPV: 27115)” for which the authors are grateful.
7. References
Bertelli D., Papotti G., Lolli M., Sabatini A.G. &Plessi M. (2008). Development of an
HS_SPME_GC method to determine the methyl anthranilate in Citrus honey. Food
Chemistry, 108, 297-303.
Bogdanov S. (2002). Harmonized methods of the International Honey Commission. Swiss
Bee Research Centre, FAM, Liebefeld, CH 3003 Bern, Switzerland.
Bogdanov S., Ruoff K. & Persano-Oddo L. (2004). Physico-chemical methods for the
characterization of unifloral honeys: a review. Apidologie, 35, 4-17.
Cano C. B., Felsner M. L., Matos J. R., Bruns R. E, Whatanabe H. M. & Almeida-Muradian,
L. B. (2001). Comparison of Methods for Determining Moisture Content of Citrus
and Eucalyptus Brazilian Honeys by Refractometry. Journal of Food Composition
and Analysis, 14, 101-109.
Carretero J. L. (1989). Análisis polínico de la miel (1st ed.). Multi-Prensa, Madrid, España.
Castro-Vazque, L., Diaz-Marot, M.C. & Pérez-Coello M.S. (2007). Aroma composition and
new chemical markers of Spanish citrus honeys. Food Chemistry, 103, 601-606.
Commission Decision 2002/657/EC of 12 August (2002). Implementing Council Directive
96/23/EC concerning the performance of analytical methods and the interpretation
of results, OJEC L221, 8-36. Brusels, Belgium.
Council Directive 2001/110 relating to honey (2002). Official Journal of the European
Communities, 12.1.2002.
DOGV (Valencian Region Regulation) No. 4167 (2002). Reglamento de la Marca de Calidad
CV para miel de azahar y romero, in: Diario Oficial de la Generalitat Valenciana. Área
de publicaciones de la Presidencia de la Generalitat Publishing.
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
81
Escriche I., Kadar M., Juan-Borrás, M. & Domenech E. (2011). Using flavonoids, phenolic
compounds and headspace volatile profile for botanical authentication of lemon
and orange honeys. Food Research International, 44 (5), 1504-1513.
Ferreres F., Giner J.M. & Tomás-Barberán F.A. (1994). A comparative study of hesperetin
and methyl anthranilate as markers of the floral origin of citrus honey, Journal of
the Science of Food and Agriculture, 65, 371-372.
Gómez-Pajuelo A. (2004). Origen botánico de la miel, In: Montagud S.A (Ed). Mieles de
España y Portugal (pp. 50-56). Barcelona, Spain.
ISO 5496 (2006). Sensory Analysis. Methodology. Initiation and training of assessors in
the detection and recognition of odours.
Jabalpurwala F.A., Smoot J. M. & Rouseff R. L. (2009). A comparison of citrus blossom
volatiles. Phytochemistry, 70, 1428-1434.
Molins J. L., Perea F., Montilla J., Martinez E. & Guerra E. (1995). Characterization du miel
d’orange (Citrus sp.) produit en Espagne, au moyen de son spectre pollinique.
Lagascalia, 18, 71-82.
Nozal M., Bernal J., Toribio L., Jiménez J. J. & Martín M.T. (2001). High-performance liquid
chromatographic determination of methyl anthranilate, hydroxymethylfurfural and
related compounds in honey. Journal of Chromatography A, 917, 95-103.
Papotti G., Bertelli D., Lolli M., Sabatini A.G. & Plessi M. (2009). Methyl anthranilate
content in Italian citrus honeys determined by HS-SPME-GC. International Journal
of Food Science and Technology, 44, 1933-1938.
Papotti G., Bertelli D. & Plessi M. (2012). Use of HS-SPME-GC-MS for the classification of
Italian lemon, orange and citrus spp. Honeys. International Journal of Food Science
and Technology, 47, 2352 -2358.
Persano-Oddo L., Piazza M.G., Sabatini A.G. &Accorti M. (1995). Characterization of
unifloral honeys. Apidologie, 26, 453-465.
Persano-Oddo L. & Piro R. (2004). Main European unifloral honeys: descriptive sheets.
Apidologie, 35, S38–S81.
Rodriguez I., Serrano S., Galán H., Ubera J. L. & Jodral M. (2010). Characterisation of Sierra
Morena citrus blossom honey (Citrus sp). International Journal of Food Science and
Technology, 45, 2008-2015.
Saenz C. & Gómez C. (2000). Mieles españolas. Características e identificación mediante
el análisis del polen. (1st ed.). Multi-Prensa, Madrid, España.
Sancho M.T., Muniategui S., Huidobro J. F. & Simal J. (1992). Aging of honey. Journal of
Agricultural and Food Chemistry, 40, 134–138.
Correlation between methyl anthranilate level and percentage of pollen in Spanish citrus honey
82
Serra-Bonvehí J. (1988). Determinación de antranilato de metilo en la miel de citricos
(Citrus sp.) del Levante Español, y su influencia en la actividad diastásica de la miel.
Alimentaria, 197, 37–40.
Serra-Bonvehí J. & Ventura F. (1995). Characterization of Citrus honey produced in Spain.
Journal of Agricultural and Food Chemistry, 43, 2053–2057.
Sesta G., Piana M.L., Persano-Oddo L., Lusco L. & Belligoli P. (2008). Methyl anthranilate
in Citrus honey. Analytical method and suitability as a chemical marker. Apidologie,
39, 334-342.
Visquert M., Vargas M. & Escriche I. (2014). Effect of postharvest storage conditions on
the colour and freshness parameters of raw honey. International Journal of Food
Science and Technology, 49, 181–187.
Von der Ohe W., Persano-Oddo L., Piana M.L., Morlot M. & Martin P. (2004). Harmonized
methods of melissopalynology. Apidologie, 35, 18-25.
White J. W. (1966). Methyl anthranilate content of citrus honeys. Journal of Food Science,
31, 102-104.
White J.W. &Bryant V.M. (1996). Assessing Citrus honey quality: pollen and methyl
anthranilate content. Journal of Agricultural and Food Chemistry, 44, 3423–3425.
Marisol Juan-Borrás, Juan Soto, Luis Gil-Sánchez, Ana Pascual-Maté, Isabel Escriche
Food Control (En revisión)
3.3. Potenciometric tongues with noble and non-noble metals
for the differentiation of Spanish honeys
considering the antioxidant capacity
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
85
Abstract
Knowledge about the antioxidant level of honey is increasingly important, due to its
implications for health, and as a marketing tool for the industry. The aim of this work was
to evaluate the capacity of a potentiometric electronic tongue, with a combination of
four noble (Au, Pt, Ir and Rh) and four non-noble metals (Ag, Ni, Co and Cu), to
differentiate between types of Spanish honey (orange blossom,
rosemary,thyme,sunflower,winter savory and honeydew honey) according to their
antioxidant level. The botanical categorization of each of the batches was carried out by
means of pollen analysis. The electronic tongue system made with Ag, Ni, Co, Cu and Au
was able to not only differentiate between types of honey but also to predict their total
antioxidant capacity. The discrimination ability of the system was proved by means of a
Neural Network Analysis fuzzy artmap type, with 100% classification success.A prediction
MLR model showed that the best correlation coefficient was for antioxidant activity
(0.9666), then for electrical conductivity (0.8959) and to a lesser extent for the other
parameters considered (aw, moisture and colour). The proposed measurement system
could be a quick, easy option for the honey packaging sector to provide continuous in line
information about a characteristic as important as the antioxidant level.
Keywords: Potentiometric tongue; honey; antioxidant capacity
1. Introduction
Honey has been a highly valued food since ancient times for its organoleptic and
therapeutic characteristics and also nowadays for its antioxidant properties. The demand
for natural antioxidants is increasing constantly as they play an important role in human
health avoiding damage caused by oxidizing agents. It is well known that honey is an
important source of natural antioxidants, one of the reasons why honey consumption is
recommended. Recent research has shown that the antioxidant properties of honey
depend on the nectar of blossoms or exudates of trees and plants visited by bees. In turn,
this is conditioned by the geographical and climatic conditions (Escricheet al., 2014). At
present, their information available to the consumer through labeling is very incomplete.
This is limited at the most to the botanical origin, allowing honey to be classified as
monofloral. There is growing interest in assessing the specific healthy properties of honey
(e.g. antioxidant level), in order to inform consumers and to increase producers profit
margins.
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
86
A large number of procedures are used to evaluate the antioxidant potential of honey.
Although these methodologies show good precision, accuracy and reliability, the problem
is that they are inappropriate for in situ monitoring. In addition, these techniques are
destructive, time-consuming and very tedious, requiring highly expert analysts and
specialized equipment (Oroian & Escriche, 2015).
Among the fastest, easy-to-handle measurement techniques that have been tested in
recent years, electronic tongues (based mainly on voltammetry, potentiometry or
impedance spectroscopy), together with multivariate chemometric tools, are the most
promising. The effectiveness of a potentiometric tongue (measuring the electrochemical
balance) made of various metals and metallic compounds (metal oxides and insoluble
metal salts) was successfully applied for the classification of honey according to its
botanical origin although it was less efficient at discriminating between applied thermal
treatments (raw, liquefied and pasteurized) (Escriche et al., 2012). Tiwari et al. (2013)
used an electronic tongue based on the electrochemical information analysis of the
voltammetric scan of a single platinum electrode as a sensor element for discrimination
between several monofloral honey samples. Ulloa et al. in 2013 described the
differentiation of various Portuguese honeys using a sensor obtained by the fusion of an
impedance electronic tongue and optical spectroscopy (UV–Vis–NIR).
The application of a commercially available potentiometric electronic tongue (Astree,
Alpha M.O.S.), either as a unique process (Major et al., 2011) or in combination with a
voltammetric electronic tongue (Wei & Wang, 2014) has been described to classify types
of honey according to botanical and geographical origin.
Using potentiometric electrodes (with metal/metal oxides and insoluble metal salts)
provides information about the presence of compounds through interaction with them
(Soto et al., 2006). For example, metal/metal oxide electrodes are sensors that afford
information about the pH of the medium, the presence of anions which form low soluble
salts with the oxidized metal and the presence of dissolved substances which can form
compounds with the corresponding metals (Escriche et al., 2012). Noble metals, such as
gold or platinum, have been used to determine the redox potential in food systems. The
measurement of the redox potential can be directly related to the content of total
reducing or oxidizing agents present in the sample. Antioxidants, such as polyphenols or
flavonoids, are compounds with reducing character that can cause changes in the
potential of metal polarization as a consequence of both their potential redox and their
relative concentrations (Soto et al., 2013). This suggests that metal sensors could be
useful for the determination of the antioxidant activity of honey.
For this reason the goal of the present study was to evaluate whether a potentiometric
electronic tongue made with a combination of metal electrodes (noble and non-noble) is
able to differentiate between types of Spanish honey according to their antioxidant level.
The specific contribution of each electrode sensor is evaluated, together with the
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
87
possibility of a correlation between the potentiometric measures of honey and the
physicochemical parameters and antioxidant capacity.
2. Materials and Methods
2.1.-Honey samples
Six types raw honey harvested in 2014 in different areas of Spain were used in this study.
They are among the most common varieties available in Spain: orange blossom (Citrus
spp.), rosemary (Rosmarinus officinalis), thyme (Thymus spp.), sunflower (Helianthus
annuus), winter savory honey (Satureja montana), and honeydew honey. Three batches
of each type of honey were directly supplied by beekeepers. All samples were sent to the
laboratory immediately after they were harvested.
2.2.-Analytical determinations
2.2.1. Melissopalynological analysis
The botanical categorization of each of the batches were performed by means of pollen
analysis. Quantification of pollen was carried out following the recommendations of the
International Commission for Bee Botany (Von Der Ohe et al., 2004). A light microscope
(Zeiss Axio Imager, Gottingen, Germany) at a magnification power of x400 with DpxView
LE image analysis software attached to a DeltaPix digital camera was used in this analysis.
2.2.2. Colour and physicochemical analysis
Color was determined using a millimeter Pfund scale C 221 Honey Color Analyzer (Hanna
Instruments). Moisture content (by refractrometry: Abbe-type model T1 Atago, USA and
the Chataway table) and electrical conductivity (by conductimetry: Crison Instrument,
Barcelona, Spain, model C830) were analyzed in accordance with the Harmonized
Methods of the European Honey Commission (Bogdanov, 2002). Water activity (aw) was
determined at 25 °C (± 0.2 °C) using an electronic dewpoint water activity meter, Aqualab
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
88
Series 4 model TE (Decagon Devices, Pullman,Washington, USA), equipped with a
temperature-control system (Chirife et al., 2006).
2.2.3. Sugar Analysis
Fructose, glucose and sucrose were analyzed as described by Bogdanov et al. (1997) using
a HPAEC high-resolution ionic chromatograph with a pulsed amperometric detector
(PAD) (Bioscan, Methrom, Switzerland) and a Metrosep Carb chromatographic column
(styrene divinylbenzene copolymer, 4.6 × 250 mm). Carbohydrates were eluted with
NaOH 0.1N at a flow rate of 1 mL min-1. Quantification of sugars was realized using
external standards constructing the corresponding calibration curves.
2.2.4. Antioxidant activity
The antioxidant activity (AA) was measured based on the scavenging activities of the
stable DPPH (2,2-diphenyl-1-picrylhydrazyl) (Sigma-Aldrich, Germany) free radical as
described by Shahidi et al. (2006). Accordingly, 1g of the honey sample (diluted in 25 mL
methanol: water, 80:20) was mixed with 3.9 mL of a methanolic solution of DPPH
(0.025mg/mL, prepared in methanol: water (80:20)). After 30 min, the absorbance of the
samples was measured at 515 nm with methanol as a blank, using a spectrophotometer
(JASCO V-630). The quantification was carried out using a standard curve of Trolox (6-
Hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid). The results were expressed as
mg of Trolox equivalent per gram of honey.All analyses were performed in triplicate.
2.2.5. Potenciometric tongue
Measurement system
Measurements were performed with tailor-made equipment formed basically by the
following parts: a set of metal electrodes, an electronic system (Martínez-Mañez et al.,
2005), a computer system for digitalization and acquisition of the signal and data analysis
software.
Set of metallic electrodes
The electrochemical measurements were obtained using an electronic tongue consisting
of an array of eight metallic electrodes (1 mm diameter from Aldrich): Noble metals (gold,
platinum, iridium and rhodium); and non-noble metals (copper, silver, nickel and cobalt).
All electrodes were placed in two homemade stainless steel tube (one for the 4 noble
metals and another for the rest) that were used as the body of the electronic tongue. The
different wire electrodes were fixed inside the tube using RS 199-1468 epoxy polymer.
Before use, the surfaces of the electrodes were polished with emery paper, and rinsed
with distilled water. Then they were polished on a felt pad with 0.05m alumina polish
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
89
from BAS, washed with distilled water and polished again on a nylon pad with 15.3 and
m diamond polishes. During the measurements, only a simple diamond polishing was
carried out.
Electronic Measurement Equipment
The measuring equipment permitted multiplexed potentiometric measures for 16
independent channels. The potential was measured automatically, sequentially and
cyclically for each of the eight electrodes with respect to the Ag/AgCl reference electrode.
The potentiometric measurements were simply the potential difference between each
electrode and the reference electrode.
Implementation of measures
To carry out a measurement, the set of eight electrodes and the reference electrode were
dipped in a vessel containing 5 g of honey in 25 mL water. Then, the multiplexed reading
of the potential was carried out for 15 min. The time required to reach equilibrium was
usually less than 10 minutes. The data used for multivariate analysis were the average
values of the last 5 min of acquisition (between 10 and 15 min). This process was repeated
three times for each of the 18 samples (6 types of honey x 3 batches).
2.3.-Statistical analysis
An analysis of variance (ANOVA) (using Statgraphics Centurion for Windows) was applied
to study the influence of the type of honey on colour, physicochemical parameters (aw,
moisture and electrical conductivity), sugars and total antioxidant activity. LSD (least
significant difference) at significance level 𝛼 = 5%was used to analyze the differences
between means.
The data matrix obtained with the potentiometric tongue was analyzed using multivariate
statistical techniques: principal component analysis (PCA) and Fuzzy ARTMAP-type
artificial neural network (ANN) were applied to evaluate the possible classification (non-
supervised and supervised, respectively) of the samples. Fuzzy ARTMAP type ANN
(Carpente et al., 1992) is a self-organizing supervised classifier that has been previously
used with nose and tongue electronic systems, with good results for a limited number of
samples, as is the case of the present work (Llobet et al., 1999). The network was
implemented in-house using function macros from basic functions of MATLAB with a
graphical environment (GUI) (Garcia-Breijo et al., 2013) using a simplified version of the
original algorithm (Kasuba, 1993). Multiple linear regression (MLR) was used to model
the relationship between the potentiometric measurement and the physicochemical
parameters. PCA and MLR were performed using the annex SOLO of the MATLAB
program (Eigenvectors Research, Inc.).
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
90
3. Results and Discussion
3.1.-Melissopalynological, colour, physicochemical
parameters, sugars and antioxidant activity
Pollen analysis of the honey samples was made as a first step in order to ascertain their
botanical origin. Table 1 illustrates the predominant pollen, colour, physicochemical
parameters, sugars and total antioxidant activity of each honey sample used in this study.
In addition, this table shows the ANOVA results (F-ratio and significant differences)
obtained for the factor “type of honey”. The information about the homogeneous groups
obtained from the ANOVA for every one of the parameters appears (in superscript letters)
in this table next to the information of the first batch of each type of honey. Taking into
account that the higher the F-ratio, the greater the effect that a factor has on a variable,
electrical conductivity, colour and total antioxidant activity were the parameters most
affected by “type of honey”. Moisture, aw, and glucose where less affected, whereas
fructose and sucrose did not show significant differences between types of honey.
Among all the honey samples considered in this study, honeydew honey, as expected,
had the highest electrical conductivity (up to 814S/cm). In fact, this honey has to have
more than 800S/cm to be classed as this type of honey.
Potenciometric tongues with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
91
Table 1. Predominant pollen of each sample, colour, physicochemical parameters, sugars, total antioxidant activity and ANOVA results (𝐹-ratio and significant
differences) obtained for the factor type of honey. The number before of each type of honey refers to the batch.
Type of honey Predominant pollen Colour
(Pfund scale) aw
Moisture g/100 g
Electrical conductivity
S/cm
Glucose g/100g
Fructose g/100g
Sucrose g/100g
Total antioxidant activity
mg trolox/100g
1. Orange 60% Citrus spp., Diplotaxis spp. , Taraxacum type 10(a) 0.585(a.b)) 17.7(c) 128(a) 32(bc) 43 1 15.81(a)
2. Orange 31% Citrus spp., Taraxacum type 6 0.591 18.0 128 32 44 1 15.81
3.Orange 60% Citrus spp., Taraxacum type,Diplotaxis spp.,
Quercus spp., Olea europaea
8 0.567 17.2 150 30 42 2 15.06
1. Rosemary 36% Rosmarinus officinalis,Taraxacum type
,Thymus spp., Anthyllis citysoides, Quercus spp.,
Citrus spp.
16(a) 0.593(b) 17.2(b,c) 178(a) 32(bc) 45 2 16.04(a)
2. Rosemary 21% Rosmarinus officinalis, Vicia spp., Echium spp. 13 0.594 17.3 118 32 44 2 16.04
3. Rosemary 17% Rosmarinus officinalis, Vicia spp., Echium spp. 0 0.568 16.9 97 30 41 1 11.72
1. Thyme 28% Thymus spp., Prunus dulcis, Centaurea spp.,
Compositae, Satureja montana, Onobrychis
viciiofolia
78(c) 0.561(a) 15.9(a) 517(c) 30(b) 42 1 356.15(c)
2. Thyme 23% Thymus spp., 2% Satureja montana,
Onobrychis viciifolia
69 0.560 15.4 392 31 44 2 257.75
3. Thyme 29 % Thymus spp., Onobrychis viciifolia 89 0.556 15.0 451 30 42 1 318.05
1. Sunflower 51 % Helianthus annuus, 23% Xanthium spp. 55(b) 0.584(b) 16.6(b,c) 316(b) 32(c) 48 2 39.91(a,b)
2. Sunflower 74% Helianthus annuus 67 0.594 16.8 369 36 46 1 51.96
3. Sunflower 70% Helianthus annuus 60 0.618 18.2 359 33 42 2 48.56
Potenciometric tongues with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
92
Table 1 (cont.)
Type of honey Predominant pollen Colour
(Pfund scale) aw
Moisture g/100 g
Electrical conductivity
S/cm
Glucose g/100g
Fructose g/100g
Sucrose g/100g
Total antioxidant activity
mg trolox/100g
1. Winter savory 35% Satureja montana, Diplotaxis spp.,
Onobrychis viciiofolia, Centaurea spp., Lavandula
latifolia, Thymus spp.
86(c) 0.573(b) 16.2(b,c) 278(b) 30(b,c) 42 1 85.56(b)
2. Winter savory 52% Satureja montana, Onobrychis viciiofolia,
Diplotaxis spp., Apiaceae, Echinops spp.,
Compositae
90 0.606 17.8 292 32 49 2 67.64
3. Winter savory 49% Satureja montana, Centaurea spp.,
Diplotaxis spp., Apiaceae, Citrus spp.
90 0.602 17.6 300 31 45 3 63.66
1. Honeydew 8% Retama sphaerocarpa, Rubus ulmifolius,
Genista type, Erica spp., Diplotaxis spp.
88(c) 0.578(a,b) 16.4(a,b) 809(d) 25(a) 42 1 55.65(b)
2. Honeydew 30% Rubus ulmifolius, 17% Echium spp., 12%
Castanea sativa, Compositae
86 0.580 16.5 809 28 46 3 57.09
3. Honeydew 49% Rubus ulmifolius, 29% Echium spp.,9%
Castanea sativa, Apiaceae, Erica spp., Labiatae,
Genista type
87 0.584 16.4 814 25 39 1 53.76
ANOVA 𝐹-ratio 115.10*** 3.35* 5.87** 170*** 7.8** n.s. n.s. 85***
∗∗∗𝑝< 0.001. For each factor, different letters in each column indicate homogeneous groups (significant differences at 95% confidence level as obtained by the LSD
test). The information about the homogeneous groups appears between brackets and superscript letters in the first row of each group
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
93
Thyme honey stands out from the other monofloral honeys due to its high electrical
conductivity values, which reached 517S/cm. This is a very high value considering its
floral origin, much higher than that observed by Karabagias et al. (2014) in thyme honey
from Greece (average value: 399 S/cm). Orange blossom and rosemary honey had the
lowest conductivity (no more than 150 and 178S/cm, respectively), without significant
differences between them.
With respect to colour, the results show that orange blossom and rosemary honey had
the lightest colour with a range of 0 to 16 mm Pfund, and winter savory honey the darkest
showing values up to 90 mm Pfund. The dark colour in honey is traditionally a highly
valued characteristic because it is well known that the darker a honey is, the higher the
mineral content (González-Miret et al., 2005). Therefore, it follows that darker honey has
higher conductivity, to the point that this relationship has been extrapolated to
antioxidant activity, however the results of the present work do not support this idea. In
fact, although the lowest values of total antioxidant activity correspond to the lightest
honeys (orange blossom and rosemary honeys), the darkest honey (winter savory honey)
did not exhibit the greatest antioxidant activity. Of all the analyzed samples, thyme honey
differs significantly in relation to antioxidant activity, as the values of this parameter were
between 4-5 times more than that of winter savory honey and 5-7 times more than
honeydew honey, traditionally considered to have a high antioxidant level.
The PCA bi-plot of scores and loading obtained considering the 18 different samples (6
types of honey x 3 batches), the physicochemical parameters and color variables is shown
in Figure 1, with the aim of evaluating the global effect of the type of honey on these
variables from a descriptive position. The predominant pollen in each sample is placed
next to the code of the botanical name.
Two principal components explained 66% of the variations in the data set: PC1 (44%) and
PC2 (22%). It can be observed that honeydew honey and thyme honey are located in the
right quadrant (although not totally differentiated), next to the highest values of colour,
electrical conductivity and antioxidant activity. The other samples on the left quadrant
without a clear distinction between them. Although the botanical origin of honey had a
clear impact on some of the parameters studied, in general terms a good differentiation
between the types of honey studied was not achieved.
Potenciometric tongues with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
94
Figure 1. PCA biplot (scores and loadings) of the variables (physicochemical parameters, sugars, color and total antioxidant activity) and honey samples:
C (orange blossom); R (rosemary); T (thyme); S (sunflower); W (winter savory honey); H (honeydew honey). Next to each sample the percentage of the
relevant predominant pollen is shown.
C-60%C-31%
C-60%
R-36%
R-21%
R-17%
T-28%
T-23%
T-29%
S-51%
S-74%S-70%
W-35%
W-52%W-49%
H-8%
Antioxidant activity
H-8%
H-8%
Colour
Electrical conductivity
aw
MoistureGlucose
Fructose
Sucrose
-3
-2,5
-2
-1,5
-1
-0,5
0
0,5
1
1,5
2
2,5
3
3,5
-2,5 -2 -1,5 -1 -0,5 0 0,5 1 1,5 2 2,5 3 3,5 4
PC
2 (
22
%)
PC1 (44%)
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
95
3.2.-Potentiometric electronic tongue information: PCA
and artificial neural networks classifications
A PCA analysis (unsupervised procedure) was applied to check if there was a spontaneous
classification from the data generated by the eight electrodes, without previously
defining the categories of the samples. Figure 2 shows the PCA biplot (scores and
loadings) from the measurements obtained with the eight electrodes together, noble and
non-noble metals. Two components explained 92% of the total variance (PC1=72% and
PC2=20%). It can be observed that a good discrimination was found between types of
honey. Samples of thyme honey and honeydew honey showed the clearest separation
between them and with the rest of the samples; PC1 promotes the best discrimination of
thyme honey and PC2 of the honeydew honey. This figure also shows that sunflower and
winter savory are well differentiated, whereas citrus and rosemary have the most similar
behavior in terms of potentiometric tongue response.
With respect to the contribution of each electrode in the calculation of PC1andPC2values,
it can be seen that two electrodes (Co and Cu) have very different behavior to the rest.
This is particularly important with regards to their influence on the PC2, since the
variation of the contribution of these two electrodes is not relevant to PC1. This figure
shows that the location of the four noble metal electrodes (Au, Pt, Ir and Rh) are very
close to each other, which implies that their behavior is quite similar. Furthermore, the
position of these four metals is relatively close to the other two non-noble metals:
especially Ag, and to a lesser extent Ni. Consequently, it was considered appropriate to
simplify the electrode system, reducing the four noble metals to one, Au, which was
selected because is easier to obtain and to work with in comparison to the other three
noble metals (Pt, Ir, Rh). Therefore, a new PCA was carried out (Figure 3) only using the
information from five electrodes: the four non-noble metals (Ag, Ni, Co, Cu) plus Au. It
can be observed that the position of the samples in Figure 3 is quite similar to Figure 2.
In the case of Figure 3 the three noble electrodes: Au, Ag and Ni, exhibited similar but
slightly different behaviour, while the other two metals(Co and Cu) still presented
different behaviour to each other and the rest. In this way it was possible to simplify the
measure system without losing information.
With the aim of complementing the information given by the PCA, an artificial neural
network analysis, as a supervised procedure, was applied. The type of neural network
used in this work was Fuzzy Artmap (Carpenter, Gossberg, Markuzon, Reynolds & Rosen,
1992) that has been previously used with nose and tongue electronic systems, with good
results for a limited number of samples (Llobet et al., 1999), as is the case of the present
work. In this analysis the available data were divided into two groups; one group was used
Potenciometric tongues with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
96
Figure 2. PCA biplot (scores and loadings) from the measurements obtained with the 8 electrodes together (4 noble and 4 non-noble metals: “Au, Pt, Ir,
Rh, Ag, Ni, Co and Cu”). C (orange blossom); R (rosemary); T (thyme); S (sunflower); W (winter savory honey); H (honeydew honey).
H1 H1
H1
H2 H2
H2H3
H3
H3
S1
S3
S1
S2S2S2
S3
S3S1
R1R1
R1
R2
R3
R2
R3
R2
R3
T1
T1
T1
T2
T2
T2T3
T3 T3
W1W1
W1
W2
W2W2
W3
W3 W3 C1
C1
C1
C3
C2
C3
C3
C2
C2
Ir
Rh
Pt
Au
Ag
Co
Cu
Ni
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
-1 -0,9 -0,8 -0,7 -0,6 -0,5 -0,4 -0,3 -0,2 -0,1 0 0,1 0,2 0,3 0,4 0,5
PC
2 (
20
%)
PC1 (72%)
Potenciometric tongues with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
97
Figure 3. PCA biplot (scores and loadings) from the measurements obtained with the 5 electrodes together (4 non-noble metals “Ag, Ni, Co and Cu”, plus
Au). C (orange blossom); R (rosemary); T (thyme); S (sunflower); W (winter savory honey); H (honeydew honey).
H1 H1
H1
H2
H2
H2H3
H3
H3
S 1
S3
S 1
S 2S 2S 2
S3
S 3
S1
R1R1
R1
R2
R3
R2
R3
R2
R3
T1
T1
T1
T2T2
T2
T3
T3
T3
W1W1
W1
W2
W2W2
W3W3
W3C1
C1C1
C3
C2
C3
C3
C2
C2
Au
Ag
Co
Cu
Ni
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
-1 -0,9 -0,8 -0,7 -0,6 -0,5 -0,4 -0,3 -0,2 -0,1 0 0,1 0,2 0,3 0,4 0,5
PC
2 (
20
%)
PC1 (72%)
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
98
for network training and obtaining the prediction model of the categories, and the other
group to verify the neural network and to estimate the degree of success in the
classification of new data into the established categories. The data distribution was as
follows: of the54 total input data 6types of honey x 3 batches x 3repetitions), two-
thirds(36) of the data were used for training and a third (18) for validation; ensuring that
each category(types of honey) was represented proportionally in both the training and
the validation groups. That is to say, for training the matrix of measurements of each
electrode was: 6 types of honey x 2 batches x 3repetitions, and for validation: 6 types of
honey x 1 batches x 3repetitions.-
The optimum model was achieved by obtaining a scan of the operating parameters of the
neural network. After that, the data of verification were applied to check the rate of
success/failures for the identification of each sample in the appropriate category. The
classification success (data not shown) was 100%. The same result was obtained
considering the starting 8 electrodes (4 noble and 4 non-noble metals). This demonstrates
that the electrode measurement system made with the 5 metals was useful for the
classification of samples of honey depending on the floral origin.
3.3.-Correlation of potentiometric data with
physicochemical parameters: MLR analysis
With the aim of verifying whether the data given by the electronic tongue system is useful
to predict relevant information provided by the physicochemical parameters, a MLR
analysis (Multiple Linear Regression) was applied. To perform the MLR analysis, the data
of 36 samples were taken to create the prediction model and the remaining 18 samples
were used to verify the performed model. That is to say, the same separation of samples
previously used in the neural network analysis.
A prediction MLR model was carried out for each the physicochemical parameters (aw,
conductivity, moisture), colour and antioxidant activity, and the potentiometric
experimental data from the metallic electrodes. The analysis was made twice, on one
hand considering the eight electrodes (4 noble metals plus 4 non-noble metals) and on
the other hand considering just the 5 selected electrodes (4 non-noble metals plus Au).
Table 2 shows the MLR results including the values of the slope, intercept, regression
coefficient and number of latent variables. A good correlation exists for total antioxidant
activity and electrical conductivity.
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
99
Table 2. MLR prediction results for the physicochemical parameters (aw, conductivity, moisture), colour and antioxidant activity
4 non-noble metals +4 noble metals
4 non-noble metals
+Au
Parameters Correlation
coefficient
Slope Intercept Correlation
coefficient
Slope Intercept
aW 0.4860 0.516 0.279 0.4790 0.445 0.319
Electrical
conductivity
0.9050 0.850 25.359 0.8959 0.813 51.126
Moisture 0.7380 0.646 5.905 0.7040 0.558 7.311
Colour 0.5300 0.634 14.529 0.7400 0.755 8.895
Antioxidant Activity 0.9665 0.970 11.162 0.9666 0.911 22.256
The best correlation coefficient was for the antioxidant activity using the 4 non-noble
metals plus Au (0.9666), although it was very similar to that obtained with all the
electrodes (0.9665). In the case of electrical conductivity, the correlation coefficients
were 0.8959 and 0.9049, respectively. A weaker correlation exists for the rest of the
parameters, especially for aw. Figure 4 shows the MLR graph corresponding to measured
values vs. predicted values of the antioxidant activity in both cases.
Figure 4. Predicted versus actual values of total antioxidant capacity given by the MLR model. A) 8 electrodes: 4 noble metals “Au, Pt, Ir and Rh” plus 4 non-noble metals “Ag, Ni, Co and Cu” and B) 4 non-noble “Ag, Ni, Co and Cu” metals plus Au.
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
100
A previous study found a remarkable correlation between physicochemical parameters
such as colour Pfund (0.9580) and diastase activity (0.9260) and data from an electronic
tongue, made of three metals (Au, Ag and Cu) and four metal compounds (Ag2O, AgCl,
Ag2CO3 and Cu2O) (Escriche et al., 2012). However, the correlation with total antioxidant
activity was much weaker (0.7660). The new electronic tongue proposed here is a great
improvement over the previous one from the point of view of its ability to differentiate
honey from the point of view of antioxidant capacity.
4. Conclusion
An electronic tongue system made of 5 metals: 4 non-noble metals (copper, silver, nickel
and cobalt) and just one noble metal (Au) is able to not only differentiate between types
of honey but also to predict their total antioxidant capacity.
The differentiation was most marked for thyme and honeydew honey and much weaker
for citrus and rosemary honey. The discrimination ability of the measurement system was
evaluated by means of an ANN fuzzy artmap type analysis, showing that the classification
success was 100%.
In summary, the proposed measurement system could be a good tool for the honey
packaging industry to provide continuous information about a factor as important as
antioxidant capacity. The importance of knowledge about this component in honey is
increasing, not only due to its implications for health, but also in terms of marketing.
The promising results obtained highlight the need to continue researching the validation
of this approach to corroborate the efficacy of the proposed electronic tongue, using a
wide variety of honey types with different botanical and geographical origins.
Acknowledgment
The authors thank the Generalitat Valenciana (Spain) for funding the project
AICO/2015/104.
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
101
5. References
Bogdanov S. (2002). Harmonized methods of the International Honey Commission Swiss
Bee Research Centre, FAM, Liebefeld, CH 3003 Bern, Switzerland.
Bogdanov S., Martin P. & Lüllman C. (1997). Harmonised methods of the European Honey
Commission. Apidologie, extra issue, 1–59.
Carpenter G. A., Gossberg S., Markuzon N., Reynolds I, & Rosen D. (1992). Fuzzy Artmap:
a neural network architecture for incremental supervised learning of analog
multidimensional maps. IEEE Trans Neural Network, 3, 698-713.
Chirife J., Zamora M. C. & Motto A. (2006). The correlation between water activity and %
moisture in honey. Fundamental aspects and application to Argentine honeys
Journal of Food Engineering, 72, 287-292.
Escriche I., Kadar M., Domenech E. & Gil-Sánchez L. (2012). A potentiometric electronic
tongue for the discrimination of honey according to the botanical origin.
Comparison with traditional methodologies: Physicochemical parameters and
volatile profile. Journal of Food Engineering, 109, 449-456.
Escriche I., Kadar M., Juan-Borrás M. & Domenech E. (2014). Suitability of antioxidant
capacity, flavonoids and phenolic acids for floral authentication of Spanish honey.
Impact of industrial thermal treatment. Food Chemistry, 142, 135–143.
Garcia-Breijo E., Garrigues J. & Gil-Sánchez L. (2013). Optimization of an Embedded
Simplified Fuzzy ARTMAP Implemented on a Microcontroller using MATLAB GUI
Environment”.Pattern Recognition: Practices, Perspectives and Challenges. Nova
Science Publishers (Hauppauge NY, EEUU).
González-Miret M .L., Terrab A., Hernanz D., Fernández-Recamales M. A. & Heredia F.J.
(2005). Multivariate correlation between color and mineral composition of honeys
and by their botanical origin.Journal of Agriculture and Food Chemistry, 6, 53(7),
2574-80
Karabagias I. K., Vavoura M. V., Nikolaou C., Badeka A. V., Kontakos S. & Kontominas M.G.
(2014). Floral authentication of Greek unifloral honeys based on the combination
of phenolic compounds, physicochemical parameters and chemometrics. Food
Research International, 62, 753–760.
Kasuba T. (1993). Simplified Fuzzy ARTMAP. Al Expert, 18-25.
Potenciometric tongues
with noble and non-noble metals for the differentiation of Spanish honeys considering the antioxidant capacity
102
Llobet E., Hines E.L., Gardner J.W., Bartlett P.N. & Mottram T.T. (1999). Fuzzy ARTMAP
based electronic nose data analysis. Sensors and actuators b-chemical, 61, 183-190.
Major N., Markovic K., Krpan M., Saric G., Hruskar M. & Vahcic N. (2011). Rapid honey
characterization and botanical classification by an electronic tongue. Talanta, 85,
569–574.
Martınez-Máñez R., Soto J., Garcia-Breijo E., Gil L., Ibáñez J. & Llobet E. (2005). An
“electronic tongue” design for the qualitative analysis of natural waters. Sensors
and Actuators, B 104, 302–307.
Oroian M. & Escriche I. (2015). Antioxidants: characterization, natural sources, extraction
and analysis. Food Research International, 74, 10-36.
Shahidi F., Liyana-Pathirana C. M. & Wall D. S. (2006). Antioxidant activity of white and
black sesame seeds and their hull fractions. Food Chemistry, 99, 478–483.
Soto J., Labrador R. H., Marcos M. D., Martınez-Máñez R., Coll C., García-Breijo E. & Gil L.
(2006). Introduction of a model for describing the redox potential in faradic
electrodes. Journal of Electroanalytical Chemistry, 594, 96–104.
Soto J., Campos I. & Martínez-Máñez R. (2013). Monitoring Wastewater Treatment Using
Voltammetric Electronic Tongues. In S.C. Mukhopadhyay & A. Mason (Eds.).Smart
Sensors for Real-Time Water Quality Monitoring (pp. 65–103). Springer- Verlag
Berlin Heidelberg.
Tiwari K., Tudu B., Bandyopadhyay R., & Chatterjee A. (2013). Identificattion of
monofloral honey using voltammetric electronic tongue. Journal of Food
Engineering, 117, 205-210.
Ulloa P. A., Guerra R., Cavaco A.M., Rosa da Costa A. M. & Figueira A.C. (2013).
Determination of the botanical origin of honey by sensor fusion of impedance e-
tongue and optical spectroscopy. Computers and Electronics in Agriculture, 94, 1-
11.
Von Der Ohe W., Persano-Oddo L., Piana M. L. Morlot M. & Martin P. (2004). Harmonized
methods of melissopalynology, Apidologie, 35, 18-25.
Wei Z. & Wang J. (2014). Tracing floral and geographical origins of honeys by
potentiometric and voltammetric electronic tongue. Computers and Electronics in
Agriculture, 108, 112-122.
Marisol Juan-Borrás, Eva Domenech, Magdalenka Hellebrandova, Isabel
Escriche
Food Research International 60, 86-94. (2014)
3.4. Effect of country origin on physicochemical, sugar and volatile
composition of acacia, sunflower and tilia honeys
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
105
Abstract
The aim of this study was to evaluate the influence of country (Spain, Romania, and Czech
Republic) and botanical origin, on the physicochemical (HMF, diastase activity, moisture
content, electrical conductivity), colour (Pfund scale and CIEL*a*b*), principal sugars
(glucose, fructose and sucrose) and volatile composition of acacia, sunflower and tilia
honeys. PCA analyses considering these variables showed that honey type had a far
greater influence on the differentiation of samples (above all due to the presence of
certain volatile compounds such as carvacrol and -terpinene for tilia honey;-pinene
and 3-methyl-2-butanol for sunflower honey, and cis-linalool oxide for acacia honey) than
geographical origin. Discriminant models obtained for each kind of botanical honey
(classified 100% for acacia and tilia honeys and 93.8% for sunflower of the cross-validated
cases) confirmed that differentiation of honeys according to their country was mainly
based on volatile compounds (For instance: 2-methyl-2-butenal and 2-methyl-2-
propanol, for acacia honeys; 1-hexanol and -pinene, for sunflower honeys and 3-
methyl-1-butanol and hotrienol, for tilia honey) and to a lesser extent on certain
physicochemical parameters such as diastase, sucrose and conductivity, respectively.
Correct classification of all samples was achieved with the exception of 10% of the
sunflower honeys from the Czech Republic. The results suggest that the presented
models are potentially useful tools for the classification of acacia, sunflower and tilia
honeys according to the country of origin.
Keywords
Acacia honey; sunflower honey; tilia honey; country origin; physicochemical parameters;
volatile compounds
1. Introduction
Consumers appreciate the possibility to choose between different unifloral honeys as
they have specific organoleptic characteristics and different attributable therapeutic
properties. Since these unifloral honeys are part of the import-export market, they offer
beekeepers and the industry the opportunity to obtain higher prices in comparison to
those without a determined botanical origin. Physicochemical properties and colour are
taken into account when the market price of honey is fixed, and they can be measured
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
106
to classify and typify the raw batches before entering the packaging process. Specifically,
colour is one of the most valuable attributes since it is considered to represent the
preferred honey flavour, and therefore directly contributes to consumer acceptability
(Visquert et al., 2014).
The physicochemical properties of honeys with the same floral source can vary to some
extent as a consequence of different climatic conditions or different geographical origins
(Anklam, 1998).The use of botanical appellation of honey together with geographical
origin is becoming a good option to protect and promote this traditional food in different
countries.
Melissopalynological characterization is commonly used for the classification of honey
according to its uniflorality, and sometimes its geographical origin. However, in some
cases the percentage of pollen is not always decisive because the production of pollen
and nectar by flowers is not always simultaneous, varying between countries and even
within the same country according to the geographical area (Feás et al., 2010a; Feás et
al., 2010b). For this reason, in addition to the quantification of pollen, the combination
of multi-component analysis and chemometric techniques is now the most efficient
approach to guarantee the authentication of honey (Anklam, 1998; Terrab et al., 2003;
Ruoff et al., 2007; Kropf et al., 2010). Among these procedures, physicochemical
(electrical conductivity,diastase activity, moisture, etc.), colour and chemical analyses
(such as sugars, among others) have been widely used in the characterization of unifloral
honeys (Persano-Oddo & Bogdanow, 2004; Ruoff et al., 2007; Escriche et al., 2011;Oroian
et al., 2013).
However, the discriminative power of the physicochemical properties and colour varies
according to the botanical origin, and the geographical and climatic conditions as a
consequence of their influence on the flowering or secretions of plants. For this reason,
as suggested by Persano-Oddo & Bogdanow, 2004, the broader the analytical scope
considered, the more accurate the classification of a specific honey.
Hence, considering that the flavour and aroma of honey are directly related to its volatile
compounds, it is reasonable to consider that volatile fraction analysis could be of great
importance to reach a better understanding of the intrinsic characteristics of honey
(Cuevas-Glory et al., 2007; Aliferis et al., 2010). The importance of this analytical
determination on its own or as a complement to the information provided by other
methodologies is reflected in different studies published in the last decade (Radovic et
al., 2001; Serra-Bonvehí & Ventura-Coll, 2003).
There are many works focused on the characterization of honey from different botanical
or geographical origins. However, to our knowledge there are no publications about the
combined use of physicochemical, sugar and volatile composition for this purpose, nor
the comparison of specific unifloral honeys (with the same botanical origin), from
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
107
different countries. For this reason, the aim of this study was to determine the influence
of the country (Spain, Romania, and Czech Republic) on the physicochemical, sugar and
volatile composition of acacia, sunflower and tilia honeys.
2. Materials and Methods
2.1.-Honey samples
A total of 80 raw unifloral honey samples (collected from beekeepers in 2011) with
different botanical origins: Acacia (Robinia pseudoacacia), sunflower (Helianthus annuus)
and tilia or lime (Tilia sp), and from different countries (Spain, Romania, and the Czech
Republic) were analysed. The acacia and sunflower honeys came from the three countries
mentioned above, whereas tilia honey was only from Romania and the Czech Republic
since it is practically inexistent in Spain. In summary, of the 80 raw samples, 30 came from
Romania (10 acacia, 10 sunflower and 10 tilia, all of them from the Transylvanian region);
another 30 came from the Czech Republic (10 acacia, 10 sunflower and 10 tilia, all of
them from the Central Bohemian region) and 20 from Spain (10 acacia from northern
Spain and 10 sunflower from central Spain).
In order to guarantee the botanical origin of the samples, the percentage of pollen was
measured for each one, following the recommendations of the International Commission
for Bee Botany (Von Der Ohe et al., 2004). A light microscope (Zeiss Axio Imager,
Göttingen, Germany) at a magnification power of x 400 with DpxView LE image analysis
software attached to a DeltaPix digital camera was used in this analysis. According to this
analysis, a honey was considered to be from acacia trees if the pollen from Robinia
pseudoacacia L. was not lower than 45%; from sunflower, if the pollen from Helianthus
annuus L. was not lower than 60% and from tilia trees if the pollen from Tilia spp. was not
lower than 45% (Saenz & Gómez, 1999; Gómez-Pajuelo, 2004; Von Der Ohe, et al.,
2004;Persano-Oddo & Piro, 2004). Samples were classified on arrival at the laboratory
and were preserved at 12ºC until they were analysed. None of the samples exhibited
signs of fermentation or granulation before initiating the analyses.
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
108
2.2.-Analytical determinations
2.2.1. Physicochemical and colour analyses
Diastase activity (Phadebas method), 5-Hydroxymethylfurfural content “HMF” (White
method), electrical conductivity (by conductimetry), and moisture content (by
refractrometry) were analysed in accordance with the Harmonized Methods of the
European Honey Commission (Bogdanov, 2002). Colour was determined using a
millimetre Pfund scale C 221 Honey Color Analyzer (Hanna Instruments) and a
spectrocolorimeter Minolta CM-3600d (Osaka, Japan). Translucency was determined by
applying the Kubelka-Munk theory for multiple scattering of the reflection spectra
(Hutchings, 1999). Colour coordinates were obtained from R, between 400 and 700 nm
for D65 illuminant and 2º observer. All tests were performed in triplicate.
Chromatic parameters Chroma (eq. 1) and hue (eq. 2), were calculated from L*, a* and
b* coordinates.
(eq.1)
(eq.2)
2.2.2.Sugar determination
Sugar (fructose, glucose and sucrose) analysis was carried out as described by Bogdanov
et al. (1997). Separation of carbohydrates took place in a HPAEC-PAD high-resolution
ionic chromatograph with a pulsed amperometric detector (PAD) (Bioscan, Methrom,
Switzerland) and a Metrosep Carb chromatographic column (styrene divinylbenzene
copolymer, 4.6 x 250 mm). Carbohydrates were eluted with NaOH 0.1N at a flow rate of
1 mL min-1. Quantification of sugars was carried out using external standards. The
corresponding calibration curves were constructed covering the values of the three
sugars which were expected to be found in the honey samples. For fructose, glucose and
sucrose, respectively, the correlation coefficients (R2) were: 0.995, 0.996 and 0.996; the
LODs (limit of detection) were: 0.01g/100g,0.01g/100g and 0.05g/100g and the LOQs
(limit of quantification) were: 0.05g/100g, 0.05g/100g and 0.1g/100g.
All analyses were carried out in triplicate.
22 *** baC ab
*
**
a
barctgh ab
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
109
2.2.3. Volatil compounds analysis
Extraction
Volatile compounds were extracted by purge and trap at 45ºC for 20 minutes and trapped
in a glass tube packed with Tenax TA (20-35 mesh), bubbling purified nitrogen (100 mL
min-1) through the sample (Escriche et al., 2011). Next, the compounds were thermally
desorbed at 220ºC for 10 minutes (at 10 mL min-1 helium flow) (TurboMatrix TD, Perkin
ElmerTM, CT-USA), then cryofocused in a cold trap at -30ºC and transferred onto the
capillary column by heating the cold trap to 250ºC (at a rate of 99ºC/s).
GC-MS analysis
A GC-MS (Finnigan TRACETM MS, TermoQuest, Austin, USA) with a DB-WAX capillary
column (SGE, Australia) (60 m length, 0.32 mm i.d., 1.0 µm film thickness) was used to
separate the volatile compounds. The carrier gas was Helium at a flow rate of 1 mL min-
1. The temperature programme was: from 40ºC (2-minute hold time) to 190ºC at 4ºC
min-1 (11-minute hold time) and finally to 220ºC at 8 ºC min-1 (8-minute hold time).
Electron impact mass spectra were recorded in impact ionization mode at 70 eV, with a
mass range of m/z 33-433. A total of 3 extracts were obtained for each sample.
2-pentanol was used as an internal standard. The identification of isolated volatile
compounds was performed by comparing their mass spectra, retention times and linear
retention indices against those obtained from authentic standards: acetic acid (ethanoic
acid);nonanal; decanal; benzaldehyde; 6-methyl-5-hepten-2-one (6-methyl-hept-5-en-2-
one); 2-methyl-3-buten-2-ol (Sigma-Aldrich, San Louis, Missouri and Acros Organics, Geel,
Belgium); 2-methyl-1-propanol (2-methylpropan-1-ol); 3-methyl-3-buten-1-ol; octane; 3-
hydroxy-2-butanone (3-hydroxybutan-2-one); 2-furanmethanol (furan-2-ylmethanol);
furfural (furan-2-carbaldehyde); dimethyl sulphide; β-linalool (3,7-dimethylocta-1,6-
dien-3-ol) (Fluka Buchs, Schwiez, Switzerland). The compounds, for which it was not
possible to find authentic standards, were tentatively identified by comparing their mass
spectra (m/z values of the most important ions) with spectral data from the National
Institute of Standards and Technology 2002 library, as well as retention indices and
spectral data published in the literature(Kondjoyan & Berdagué, 1996; Radovic et al.,
2001; Soria et al., 2004; De la Fuente et al., 2005; Bianchi et al., 2005; Alissandrakis et al.,
2005). A mixture of a homogenous series of alkanes (C8-C20 by Fluka Buchs, Schwiez,
Switzerland) was injection into the Tenax in the same temperature-programmed run, as
described above in order to determine the Kovàts retention indices of all the compounds.
Due to fact that was not possible to obtain authentic commercial standards for all the
identified compounds, the variables used in the statistical analysis for differentiation
between honeys corresponded to semiquantified compounds. These data were
calculated g/100 g of honey) using the amount of internal standard, the relative area
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
110
between the peak areas of each compound and the peak area of the internal standard,
assuming a response factor equal to one (Castro-Vazquez et al., 2009).
2.3.-Statistical analysis
A multifactor analysis of variance (ANOVA) (using Statgraphics Centurion for Windows)
was carried out to study the influence of the type of honey and the country of harvesting
on the physicochemical parameters, colour, sugars and volatile compounds. The method
used for multiple comparisons was the LSD test (least significant difference) with a
significance level α= 0.05. In addition, data were analyzed using a Principal Component
Analysis (PCA) applying the software Unscrambler X.10 and a Stepwise Linear
Discriminant Analyses (SLDA) using “forward” procedure (SPSS 16.0). This analysis selects
the variables that allow differentiation between honeys. The classification functions
corresponding to each group of honeys were calculated. The statistical F function was
used as a criterion for variable selection.
3. Results and Discussion
3.1.-Physicochemical, colour and sugar analyses
Table 1 shows the results of the analysis of the three types of honey harvested in the
different countries: the average values and standard deviation of the physicochemical
parameters (HMF, diastase activity, moisture content, electrical conductivity); colour
(Pfund and CIEL*a*b*); the percentage content of the principal sugars (glucose, fructose
and sucrose) and the fructose/glucose ratio. In addition, this table shows the ANOVA
results (F-ratio and significant differences) obtained for the factors “type of honey” and
“country”. For the country factor, each type of honey was considered separately.
Although raw honey was used, hydroximethylfurfural (HMF) was evaluated to
corroborate the freshness. All the analyzed honeys complied with the international
maximum limit of 40 mg/kg (Council Directive 2001/110 relating to honey, 2002). Acacia
honey had the lowest average values (from 3.3 to 7.2mg/kg), and sunflower honey,
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
111
especially from Romania, and the Czech Republic, had surprisingly high average values
(23.4 and 21.9mg/kg, respectively), taking into account that they were fresh, non-
thermally treated samples. These values are in accordance with Kádár et al. (2010) and
Oroian (2012).
Diastase is one of the most important enzymes in honey. Its concentration varies not only
according to its botanical origin, but also due to aging and extreme temperatures (Fallico
et al., 2006). In this study samples ranged from 8.7 ºGoethe in acacia honey from the
Czech Republic to 19.1 ºGoethe for Spanish sunflower honey. All the samples complied
with the Council Directive 2001/110 relating to honey (2002), which stipulates that these
types of honeys should have a value higher than 8ºGoethe. The only exception is acacia
honey for which a minimum of 3.1ºGoethe is admitted, as it is considered to have low
enzyme content. However, in this paper such low values were not found in any of the
analyzed acacia honeys.
Honey moisture content, which can vary from year to year, depends not only on
environmental conditions, but also on beekeeping practices (Acquarone et al., 2007).
Taking into account the fact that the moisture content of honey has to be lower than 20
g/100g (Council Directive 2001/110 relating to honey, 2002), the values obtained in this
work were satisfactory as they ranged from 15.3g/100g in sunflower honey from Spain
to 17.5g/100g in tilia honey from Romania. Spanish acacia and sunflower honeys showed
the best moisture values, lower than 16g/100g.
As expected, tilia honey had the highest levels of conductivity, with average values of 0.80
and 0.50 mS/cm from the Czech Republic and Romania, respectively. Values higher than
0.80mS/cm are not acceptable for floral honeys in general; however there are some
specific honeys that can exceed this value. This is the case of tilia honey and others such
as Calluna, Erica or Arbustus, because of the mineral content of these honeys. On the
contrary, the low level of minerals in acacia honey is reflected by its low electrical
conductivity (0.17-0.19 mS/cm) (Feás et al., 2010a). No significant differences were
observed between countries.
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
11
2
Table.1 Physicochemical parameters, colour and principal sugars (average values and standard deviation) in acacia, sunflower and tilia honeys harvested in different countries: Spain (Sp), Romania (Ro), and Czech Republic (Cz). ANOVA results (F-ratio and significant differences) obtained for two factors: country and type of honey. For the country factor, each type of honey was considered separately.
ns: Non significant; * p<0.05; ** p<0.01; ***p<0.001
Physico-chemical
Parameters
COUNTRY FACTOR TYPE OF HONEY FACTOR
Acacia Sunflower Tilia
Acacia
Sunflower
Tilia
F-ratio Sp Ro Cz F-ratio Sp Ro Cz F-ratio Ro Cz F-ratio
HMF
(mg kg-1) 3.3(1.8) 7.2(11.9) 3.3(2.2) 0.79ns 16.4(3.5) 23.4(0.4) 21.9(7.9) 0.57ns 7.1(6.5) 18.8(14.9) 2.22ns 4.8(7.6) 21.3(7.0) 15.5(13.9) 15.17***
Diastase activity
(ºGoethe) 17.3(4.8) 10.4(4.6) 8.7(1.7) 9.93*** 19.1(1.2) 10.1(0.8) 11.9(3.7) 4.33* 8.8(0.9) 14.6(5.2) 4.66ns 11.3(4.9) 12.7(4.2) 12.9(5.1) 0.66ns
Moisture (g/100g) 15.9 (0.2) 16.9(1.5) 17.0(1.0) 1.91ns 15.3(0) 17.3(0.14) 16.6(1.04) 2.33ns 17.5(0.12) 16.4(1.05) 3.92ns 16.7(1.16) 16.5(1.0) 16.7(1.1) 0.24ns
Conductivity (μS cm-1) 0.19(0.04) 0.17(0.03) 0.17(0.08) 0.32ns 0.44(0.0) 0.35(0.0) 0.43(0.12) 0.52ns 0.50(0.08) 0.80(0.12) 18.29** 0.17(0.05) 0.42(0.10) 0.71(0.17) 106.37***
Colour
Pfund 9.1(2.5) 10.6 (2.2) 4.3 (1.3) 1.7ns 66.7 (0.5) 51.0 (3) 53.2 (14.6) 0.86ns 37.3(3.9) 42.2(17) 0.15ns 6.9(4.3) 56.3(12.6) 40.8(13.7) 24.8***
L* 50.5(4.3) 56.6(5.8) 54.6(1.8) 2.22ns 43.6(3.4) 48.1(0.1) 44.9(3.5) 0.53ns 49.6(1.2) 48.3(7.6) 0.05ns 54.1(4.7) 44.9(3.3) 48.6(6.2) 9.86***
Chroma (C*ab) 19.6(3.1) 17.8(5.9) 17.8(2.6) 1.60ns 22.9(4.6) 27.4(2.5) 23.8(5.2) 0.27ns 27.2(1.3) 24.5(4.3) 0.68ns 17.3(4.4) 24.1(4.5) 25.4(3.7) 10.36***
Hue (h*ab) 84.4(9.1) 93.2(7.2) 94.9(3.6) 2.96ns 70.4(2.8) 78.5(3.4) 74.6(8.3) 1.56ns 82.7(2.,2) 81.2(7.9) 0.06ns 91.3(7.7) 74.0(6.9) 81.6(6.2) 14.98***
Sugars (g/100g)
Glucose 26.8(2.7) 26.9(2.8) 31.0(4.5) 7.86** 37.1(0.8) 33.9(1.62) 38.3(7.2) 1.33ns 29.7(0.8) 33.1(1.4) 18.8*** 28.5(4.4) 36.3(6.6) 32.2(2.02) 24.49***
Fructose 40.2(3.6) 45.7(2.8) 49.2(6.6) 8.16** 39.3(0.6) 39.5(0.98) 43.0(6.9) 0.68ns 41.3(1.6) 41.9(1.4) 0.38ns 45.2(6.) 40.3(6.1) 41.7(1.5) 3.16ns
Sucrose 2.2(0.8) 1.7(0.6) 1.6 (0.3) 1.63ns 1.04(0.54) 0.60(0.37) 0.7(0.9) 4.92* 0.9(0.4) 1.5(0.1) 0.15ns 1.7(0.5) 0.8(0.5) 0.3(0.2) 7.80**
Fructose/Glucose ratio 1.5(0.1) 1.7(0.2) 1.5 (0.1) 4.49* 1.0(0.1) 1.16(0.02) 1.1(0.1) 3.48ns 1.3(0.1) 1.2(0.03) 38.68*** 1.6(0.17) 1.06(0.07) 1.3(0.06) 68.28***
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
113
With regard to colour, semi-qualitative Pfund scale and colour coordinates CIEL* a* b*
were measured (Table 1). CIEL* a* b*colour coordinates, and chromatic parameters (hue
and chroma), are not commonly regulated. However, they are often used in research
studies to supplement the information provided by the Pfund scale. In this work Pfund
values ranged from 4.3 mm for the acacia honey from the Czech Republic to 66.7 mm for
sunflower honey from Spain. Acacia honey is characterized by a very light colour together
with low conductivity. This is logical as honey colour is mainly related to mineral content.
Light coloured honeys usually have low mineral levels, while dark coloured honeys
normally have high mineral content (Al et al., 2009).
In relation to CIEL* a* b* values, acacia honey had the highest lightness (especially from
Romania: L*= 56.6), a yellowish hue (average value of 91.3) and the lowest chroma
(average value of 17.3) which is associated with the lowest colour purity. On the contrary,
sunflower honey was the darkest (lowest L*, with an average value of 44.9), with the
same chroma as tilia honey and the lowest hue of the three types of analyzed honeys:
74.0, 81.6 and 91.3 for sunflower, tilia and acacia, respectively. In general, the tilia honey
had intermediate L* values (average= 48.6), lower than those found by Kropf, et. al., 2010
(between 60.3 and 62.3), who analyzed this type of honey from three different
geographical regions of Slovenia.
In general, the colour values obtained with the Pfund scale as well as CIEL*a*b, were as
expected for these varieties of honey (Persano-Oddo et al., 1995; Piazza& Persano-Oddo,
2004).
The sugar composition of honey depends of the type of flowers used by the bees, and
therefore varies according to the type of honey and geographical and climatic conditions
(Mateo & Bosch-Reig, 1998; Al et al., 2009; Kaskonienè et al., 2010). For this reason, the
level of some sugars and even the ratios between them are used to ascertain honey
authenticity (Nozal et al., 2005). As expected, fructose was the most dominant sugar
followed by glucose in all cases (Persano-Oddo & Piro, 2004). Acacia had high fructose
(49.2g/100g for acacia from the Czech Republic) and low glucose content (26.8g/100g for
the acacia honey from Spain). Acacia honeys showed the highest sucrose content, as
reported by Persano-Oddo et al. in 1995. In this study the Spanish acacia had the highest
sucrose level: 2.2 g/100g. Sunflower had a very high glucose level (average=36.3 g/100g)
compared to both the other honeys and therefore a very low F/G ratio (average=1.06).
In respect to the fructose-glucose ratio F/G ratio, acacia and tilia honeys are characterized
by high F/G values in contrast to sunflower honeys, as reported in previous works
(Persano-Oddo et al., 1995) and as established by European legislation (Council Directive
2001/110 relating to honey, 2002). The values obtained in the present work
(averages=1.6, 1.3 and 1.06 for acacia, tilia and sunflower) are in accordance with these.
Besides that, it is important to point out that the fructose-glucose ratio (F/G) indicates
whether a honey will granulate; the lower the ratio, the quicker the crystallization.
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
114
Accordingly, the order of crystallization of the three types of honey analyzed in this study
is: sunflower honey (F/G= 1.06), tilia honey (F/G= 1.3), and acacia honey (F/G= 1.6).
Almost all the physicochemical, colour and sugar parameters differed significantly
between the three botanical types of honey studied. However, considering each type of
honey separately, significant differences between countries were only found for diastase
activity (for acacia and sunflower honeys), conductivity (for tilia honey) as well as for
some sugars (glucose for acacia and tilia, fructose for acacia, and sucrose for sunflower).
In the same way, the F/G ratio differed significantly between countries for acacia and tilia.
In order to evaluate the global effect of the type of honey on the physicochemical
parameters, colour, and F/G ratio from a descriptive point of view, a principal component
analysis (PCA) was performed. Figure 1 shows the PCA bi-plot of scores and loading
obtained considering the eight analysed honeys and the different parameters. The values
of HMF and moisture were not taken into account, as both are mainly related to the
quality of honey and not to the botanical origin, and therefore are not useful for
differentiation between honeys. This analysis was carried out considering the average
values of each parameter obtained from each type of honey and country (the code for
each point in the figure corresponds to: kind of honey-country). In the score plot,
proximity between samples reflects similarity in relation to the analysed parameters.
Two principal components explained 74% of the variations in the data set: PC1 (55%) and
PC2 (19%). The first principal component differentiates the three kinds of honeys to a
certain extent. Acacia located on the left was differentiated clearly from the others, while
sunflower and tilia on the right, are not so obviously separated from each other. This
indicates that although the botanical origin of honey has an influence on the parameters
studied, these are not sufficient for differentiation between the three varieties studied
here. On the other hand, the country seems to imply a minor effect on the analysed
parameters as the samples were principally grouped according to type of honey.
3.2.-Volatile compounds
The average values and standard deviation of the volatile compounds analyzed in the
three types of honey harvested in the different countries are showed in Table 2. Of the
51 identified compounds, only 17 compounds in acacia honey, 9 in sunflower and 8 in
tilia honeys, showed significant differences between countries. However, considering the
type of honey as a factor, significant differences were found for 45 volatile compounds.
Another PCA (Figure 2) was conducted to evaluate the global effect of the type of honey
and country, but in this case for the volatile compounds. The distribution of the samples
was similar but clearer than the previous bi-plot obtained from the FQ parameters. In this
case, the first principal component clearly differentiates acacia honey (bottom left
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
115
quadrant) from tilia (bottom right quadrant) and the second principal component
differentiates quite well between sunflower (upper quadrants) and acacia and tilia
honeys (lower quadrants).
The loading plot shows that certain compounds are to some extent responsible for this
differentiation. This is the case of compounds such as carvacrol (Lusic et al., 2007;
Plutowska et al., 2011) and -terpinene (Radovic et al, 2001) which were attributed as
markers for tilia honey, and were only found in this kind of honey in this work. Plutowska
et al. (2011), also only identified -terpinene in tilia honeys, when analyzing 7 varieties
of honeys. The same occurs for other compounds, such as-pinene and 3-methyl-2-
butanol in the case of sunflower, and cis-linalool oxide in the case of acacia which were
essential in this work to differentiate these honeys. This is in line with (Radovic et al.,
2001) for these two varieties of honey.
The aforementioned authors reported phenylacetaldehyde as a typical volatile
compound for acacia honeys and phenylethyl alcohol for tilia honey, though this is not
consistent with this study, nor with others (Plutowska et al., 2011).
As observed before for physicochemical parameters, volatile compounds seem to
contribute more to the differentiation of honey according to botanical origin, than
country of origin.
However, it is logical that honeys with the same botanical origin (Robinia pseudoacacia
in the case of acacia honeys, Helianthus annuus in the case of sunflower honeys and Tilia
sp in the case of tilia honeys), but from different countries are relatively similar.
Nevertheless, there are obvious differences between the geographic sources which could
be attributed to climatic conditions, but above all to the surrounding flora. The nectar of
other plants may contribute to the variability of the analysed parameters:
physicochemical, sugar and volatile compounds. This should not be considered a negative
aspect; instead it confers a certain singularity to the same type of honey with different
geographical origins.
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
11
6
Figure 1. Biplot for the two principal components of the PCA model for the physicochemical parameters, sugars (fructose “F”, glucose “G” sucrose “S” and F/G ratio) and colour (Pfund and CIEL*a*b) in acacia, sunflower and tilia honeys harvested in the different countries: Spain (Sp), Romania (Ro), and Czech Republic (Cz).
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
11
7
Table.2. Volatile compounds (average values and standard deviation) in acacia, sunflower and tilia honeys harvested in different countries: Spain (Sp), Romania (Ro), and Czech Republic (Cz). ANOVA results (F-ratio and significant differences) obtained for two factors: country and type of honey. For the country factor, each type of honey was considered separately.
COMPOUNDS
COUNTRY FACTOR TYPE OF HONEY FACTOR
RI ACACIA SUNFLOWER TILIA AC SUN TIL
ANOVA F ratio
Sp Ro Cz ANOVA F ratio
Sp Ro Cz ANOVA F ratio
Ro Cz ANOVA F ratio
ACIDS
Ethanoic acid 1584 0.01(0.02) <0.001 0.03(0.04) 3.80* 0.12(0.06) 0.19(0.13) 0.22(0.18) 0.5ns 0.02(0.02) 0.13(0.12) 3.17ns 0.01c 0.20a 0.10b 15.11***
Propanoic acid 2-methyl- 1697 0.06(0.02) 0.04(0.04) 0.01(0.01) 5.93*** <0.001 <0.001 <0.001 - <0.001 <0.001 - 0.03a 0.00b 0.00b 15.53***
ALDEHYDES
3-Methyl-butenal 935 <0.001 <0.001 <0.001 - <0.001 <0.001 <0.001 - 0.21(0.12) 0.19(0.08) 0.20ns 0.00b 0.00b 0.19a 84.27*** 2-Pentanal 937 0.03(0.01) 0.04(0.02) 0.02(0.00) 2.60ns <0.001 <0.001 <0.001 - <0.001 <0.001 - 0.03a 0.00b 0.00b 61.41***
2-Methyl-2-butenal 1129 0.06(0.03) 0.01(0.00) 0.02(0.01) 10.70*** 0.13(0.01) 0.09(0.0) 0.14(0.18) 0.09ns 0.02(0.04) 0.13(0.06) 9.80** 0.02b 0.13a 0.02b 9.70***
3-Methyl-2-butenal 1236 0.07(0.02) 0.08(0.12) 0.06(0.01) 0.18ns 0.13(0.01) 0.05(0.00) 0.07(0.06) 1.23ns 0.02(0.00) 0.02(0.01) 0.24ns 0.07a 0.08a 0.09a 0.67ns
Octanal 1417 0.01(0.00) 0.01(0.00) <0.001 2.69ns <0.001 <0.001 <0.001 - <0.001 <0.001 - 0.01a 0.00b 0.00b 52.91***
Nonanal 1523 0.07(0.01) 0.06(0.04) 0.04(0.01) 2.27ns 0.02(0.01) 0.03(0.0) 0.03(0.02) 0.19ns 0.09(0.02) 0.18(0.13) 1.50ns 0.05b 0.03b 0.15a 14.73***
Decanal 1630 0.01(0.00) 0.03(0.01) 0.02(0.00) 8.11** 0.04(0.0) 0.04(0.0) 0.03(0.02) 0.03ns 0.04(0.00) 0.04(0.02) 0.48ns 0.02b 0.03a 0.04a 7.34**
Benzaldehyde 1675 0.13(0.06) 0.25(0.15) 0.20(0.05) 2.15ns 0.12(0.02) 0.14(0.01) 0.13(0.09) 0.02ns 0.14(0.05) 0.37(0.43) 1.04ns 0.2ab 0.13b 0.31a 2.42ns
ALCOHOLS
2-Methyl-2-propanol 920 0.03(0.02) 0.04(0.04) 0.10(0.02) 8.07** <0.001 <0.001 <0.001 - <0.001 <0.001 - 0.06a 0.00b 0.00b 25.74***
2-Propanol 947 0.03(0.01) 0.10(0.12) 0.02(0.01) 2.91ns 0.25(0.14) 0.19(0.01) 0.21(0.12) 0.11ns <0.001 <0.001 - 0.05b 0.21a 0.00c 26.23***
Ethanol 956 0.40(0.20) 0.56(0.75) 0.38(0.33) 0.32ns 0.51(0.35) 0.38(0.06) 0.79(0.98) 0.23ns 1.25(0.24) 0.73(0.56) 3.01ns 0.45b 0.69ab 0.88a 2.25ns
2-Butanol 1047 0.20(0.14) 0.03(0.02) 0.05(0.13) 5.19* 0.73(0.90) 0.01(0.0) 0.12(0.21) 3.11ns 0.25(0.08) 0.06(0.02) 50.84*** 0.08a 0.19a 0.11a 1.2ns
2-Methyl-3-buten-2-ol 1063 0.11(0.05) 0.11(0.12) 0.16(0.06) 0.89ns <0.001 <0.001 <0.001 - 0.62(0.48) 0.14(0.06) 10.29** 0.13b 0.00c 0.28a 8.84***
2-Methyl-3-buten-1-ol 1062 <0.001 <0.001 <0.001 - 0.26(0.00) 0.20(0.06) 0.21(0.21) 0.08ns 0.31(0.07) 0.30(0.12) 0.01ns 0.00b 0.21a 0.00b 30.8***
2-Methyl-1-propanol 1119 0.05(0.02) 0.07(0.07) 0.05(0.02) 0.47ns 0.05(0.0) 0.01(0.0) 0.01(0.01) 5.60* 0.27(0.08) 0.16(0.10) 3.52ns 0.06b 0.01a 0.19a 28.99***
3-Methyl-2-butanol 1137 <0.001 <0.001 <0.001 - 0.42(0.04) 0.25(0.01) 0.36(0.04) 6.2* 0.01(0.00) 0.00(0.00) 1.16ns 0.00b 0.36a 0.00b 61.12***
1-Butanol 1175 0.10(0.03) 0.10(0.10) 0.08(0.02) 0.34ns 0.33(0.27) 0.15(0.01) 0.39(0.37) 0.41ns 0.11(0.02) 0.06(0.05) 2.55ns 0.09b 0.35a 0.08b 11.56***
3-Methyl-1-butanol 1233 <0.001 <0.001 <0.001 - <0.001 <0.001 <0.001 - 0.61(0.04) 0.30(0.10) 32.03*** 0.00b 0.00b 0.39a 114.95***
2-Penten-1-ol 1268 0.00(0.00) 0.14(0.10) 0.05(0.04) 8.18** <0.001 <0.001 <0.001 - <0.001 <0.001 - 0.07a 0.00b 0.00b 10.72***
3-Methyl-3-buten-1-ol 1277 0.19(0.04) 0.11(0.08) 0.16(0.04) 1.59ns 0.44(0.02) 0.21(0.01) 0.50(0.21) 1.93ns 0.31(0.02) 0.30(0.11) 0.01ns 0.15c 0.45a 0.31b 27.38***
2-Heptanol 1449 <0.001 <0.001 <0.001 - 0.03(0.03) 0.01(0.0) 0.04(0.05) 0.37ns <0.001 <0.001 - 0.00b 0.04a 0.00b 16.78***
2-Methyl-2-buten-1-ol 1449 0.14(0.03) 0.06(0.05) 0.13(0.05) 7.15** 0.09(0.00) 0.16(0.0) 0.15(0.09) 0.42ns 0.16(0.01) 0.19(0.13) 0.09ns 0.11b 0.14ab 0.19a 3.96*
1-Hexanol 1476 <0.001 <0.001 <0.001 - 0.10(0.0) 0.01(0.0) 0.01(0.01) 36.67*** 0.02(0.00) 0.04(0.04) 0.52ns 0.00b 0.03a 0.03a 12.69***
3-Hexen-1-ol 1511 0.01(0.00) 0.03(0.04) 0.00(0.00) 2.97ns <0.001 0.02(0.0) 0.01(0.0) 13.07** <0.001 <0.001 - 0.018a 0.012ab 0.00b 3.54*
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
11
8
Table 2 (cont.)
ns: Non significant; * p<0.05; ** p<0.01; ***p<0.001
COMPOUNDS
COUNTRY FACTOR TYPE OF HONEY FACTOR
RI ACACIA SUNFLOWER TILIA AC SUN TIL
ANOVA F ratio
Sp Ro Cz ANOVA F ratio
Sp Ro Cz ANOVA F ratio
Ro Cz ANOVA F ratio
KETONES
Acetone 836 0.45(0.27) 0.28(0.16) 0.09(0.01) 9.35** 0.33(0.05) 0.45(0.12) 0.23(0.08) 5.32* 0.85(0.27) 1.05(0.51) 0.54ns 0.24b 0.27b 0.99a 35.41***
2-Butanone 921 <0.001 <0.001 <0.001 - 0.66(0.56) 0.17(0.03) 1.19(1.72) 0.40ns 0.10(0.03) 0.42(0.18) 10.73** 0.00b 0.97a 0.33b 7.38**
2-Pentanone 1003 <0.001 <0.001 <0.001 - 0.13(0.11) 0.00(0) 0.26(0.32) 0.74ns 0.37(0.05) 0.57(0.31) 1.65ns 0.00c 0.21b 0.51a 30.11***
3-Hepten-2-one 1020 <0.001 <0.001 <0.001 - <0.001 <0.001 <0.001 - 0.03(0.00) 0.08(0.04) 4.65ns 0.00b 0.00b 0.07a 46.32***
2-Heptanone 1212 <0.001 <0.001 <0.001 - 0.02(0.00) 0.01(0.00) 0.00(0.0) 1.48ns <0.001 <0.001 - 0.00b 0.01a 0.00b 20.64***
3-Hydroxy-2-butanone 1425 0.06(0.03) 0.10(0.18) 0.01(0.00) 1.69ns 0.25(0.02) 0.10(0.02) 0.05(0.05) 13.43** 0.19(0.05) 0.31(0.16) 2.26ns 0.05b 0.08b 0.28a 16.42***
6-Methyl-5-hepten-2-one 1469 0.00(0.00) 0.01(0.01) 0.00(0.01) 0.80ns 0.02(0.0) 0.0(0.0) 0.19(0.0) 24.88*** 0.01(0.00) 0.01(0.00) 0.67ns 0.01b 0.018a 0.01b 3.96*
Ethanone-1-4-methyl phenyl
1869 <0.001 <0.001 <0.001 - <0.001 <0.001 <0.001 - 0.06(0.02) 0.32(0.25) 4.07ns 0.001b 0.001b 0.25a 21.65***
HYDROCARBONS
Octane 802 0.07(0.04) 0.03(0.01) 0.03(0.0) 7.14** 0.12(0.01) 0.06(0.01) 0.04(0.06) 1.51ns <0.001 <0.001 - 0.04a 0.05a 0.00b 10.2***
Nonane 902 0.01(0.00) 0.01(0.00) 0.01(0.00) 0.38ns <0.001 <0.001 <0.001 - <0.001 <0.001 - 0.01a 0.00b 0.00b 125.97***
n-Decane 1004 0.17(0.05) 0.16(0.05) 0.10(0.03) 5.16* 1.88(0.85) 1.00(0.0) 1.69(1.62) 0.22ns <0.001 <0.001 - 0.14b 1.62a 0.00b 24.58***
Toluene 1069 0.07(0.02) 0.06(0.05) 0.08(0.02) 0.63ns <0.001 <0.001 <0.001 - 0.06(0.02) 0.12(0.13) 0.75ns 0.07a 0.00b 0.11a 10.86***
p-Xylene 1164 <0.001 <0.001 <0.001 - <0.001 <0.001 <0.001 0.00 0.22(0.10) 0.25(0.34) 0.50ns 0.01a 0.00a 0.18a 2.11ns
ESTERS
Ethyl acetate 909 0.01(0.00) 1.42(1.36) 0.01(0.00) 8.32* <0.001 <0.001 <0.001 - 1.17(0.91) 1.63(0.92) 0.71ns 0.55b 0.00b 1.50a 10.51***
Acetic acid butyl-ester 1098 0.05(0.02) 0.02(0.03) 0.11(0.05) 11.91*** <0.001 <0.001 <0.001 - <0.001 <0.001 - 0.06a 0.001b 0.001b 21.53*** SULFUR COMPOUNDS
Dimethyl sulphide <800 0.09(0.06) 0.08(0.08) 0.16(0.06) 3.20ns 0.62(0.16) 0.54(0.17) 0.40(0.20) 1.25ns 0.35(0.08) 0.27(0.23) 0.43ns 0.11c 0.30b 0.45a 21.92***
Dimethyl disulfide 1104 <0.001 <0.001 <0.001 - <0.001 <0.001 <0.001 - 0.08(0.03) 0.32(0.37) 1.54sn 0.00b 0.00b 0.25a 11.63***
FURANES
Furanmethanol 1576 0.06(0.05) 0.43(0.52) 0.08(0.04) 3.57* <0.001 <0.001 <0.001 - <0.001 <0.001 - 0.21a 0.00b 0.00b 4.84*
Furfural 1606 0.32(0.06) 0.56(0.47) 0.18(0.06) 4.04* 1.13(0.17) 1.38(0.18) 0.71(0.29) 5.93* 1.15(0.11) 1.33(0.94) 0.14ns 0.36c 0.86b 1.28a 16.07***
TERPENES
Carvacrol 1803 <0.001 <0.001 <0.001 - <0.001 <0.001 <0.001 - 0.45(0.16) 1.32(0.18) 5.2* 0.00b 0.00b 1.07a 20.68***
α-Terpinene 1267 <0.001 <0.001 <0.001 - <0.001 <0.001 <0.001 - 0.45(0.34) 0.10(0.05) 10.94** 0.00b 0.00b 0.20a 14.8***
α-Pinene 1024 <0.001 <0.001 <0.001 - 0.05(0.02) 0.08(0.02) 0.33(0.12) 5.80* <0.001 <0.001 - 0.00b 0.25a 0.00b 4.58*
Borneol 1822 <0.001 <0.001 <0.001 - <0.001 <0.001 <0.001 - 0.11(0.07) 0.22(0.18) 1.41ns 0.00b 0.00b 0.19a 29.72***
β-Linalool 1670 0.06(0.03) 0.09(0.04) 0.04(0.01) 5.09* <0.001 <0.001 <0.001 - <0.001 <0.001 - 0.07a 0.00b 0.00b 51.32***
Hotrienol 1737 0.71(0.27) 0.39(0.49) 0.07(0.02) 2.66** 0.15(0.04) 0.24(0.03) 0.39(0.40) 0.41ns 0.24(0.02) 0.91(0.48) 7.33* 0.34b 0.33ab 0.72a 2.84ns
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
11
9
Figure 2. Biplot for the two principal components of the PCA model for the volatiles compounds identified in acacia, sunflower and tilia honeys
harvested in the different countries: Spain (Sp), Romania (Ro), and Czech Republic (Cz).
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
120
3.3.-Identification of the variables with the highest
discriminant power
The information provided by both ANOVA and PCA analyses carried out for
physicochemical parameters and volatile compounds, shows that certain variables are to
some extent more important in the differentiation of honeys. To discern which variables
contribute the most to the differentiation of honeys from different countries but from
the same botanical origin a discriminant analysis was applied separately for every
botanical type of honey (acacia, sunflower and tilia).
Only the variables with significant differences between countries (in ANOVA results) were
included in the models. These models, obtained using the physicochemical and volatile
compound variables jointly and applying a stepwise method, permitted the classification
of 100% of acacia and tilia honeys and 93.8% of sunflower for the cross-validated cases.
Kadar et al. in 2011 reported that a discriminant model obtained with volatile compounds
and physicochemical parameters used jointly and applying a cross-validated procedure
was effective for the differentiation of two types of honeys (between lemon blossom
honey and orange blossom honey).
Table 3 shows the standardized canonical discriminant function coefficients obtained in
the selected models for every type of honey. In the construction of the two discriminant
functions, different variables were used in each case. Specifically, 7, 6 and 3 volatile
compounds and 1 physicochemical parameter (diastase activity, sucrose and
conductivity) for acacia, sunflower and tilia honeys, respectively.
The higher the absolute value of a standardized canonical coefficient, the more significant
a variable is. The first canonical function was the one that discriminated best between
honey groups, given that it represented the highest variability. Accordingly, the variables
that most contributed to the discrimination of honeys according to their country of origin
were: for acacia honeys (which function 1 explained 88.2% of the total variance), 2-
methyl-2-butenal, 2-methyl-2-propanol and acetic acid butyl ester; for sunflower honeys
(function 1 explained 94.3%), 1-hexanol, sucrose andpinene; and for tilia honey
(function 1 explained 100%), 3-methyl-1-butanol, hotrienol y 2-butanone. It should be
highlighted that despite the appearance of a physicochemical variable in each model, this
was not the one which contributed the most in any case.
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
121
Table 3. Standardized canonical discriminant function coefficients
Acacia honey Variables
Function 1 88.8%
Function 2 11.2%
Diastase activity 1.588 1.304
Octane 2.592 0.575
2-Methyl-2-propanol -2.815 0.533
2-Butanol 0.693 0.443
Acetic acid butyl ester 2.380 -0.905
2-Methyl-2-butenal 4.148 2.179
2-Penten-1-ol 1.699 0.876
2-Methyl-2-buten-1-ol -1.402 -2.546
Sunflower honey Variables
Function 1 94.3%
Function 2 5.7%
Sucrose 17.416 9.760
-Pinene -16.045 -4.218
2-Methyl-1-propanol -5.927 7.059
3-Hydroxy-2-butanone 4,744 -4.320
6-Methyl-5-hepten-2-one 11.603 4.685
1-Hexanol 22.218 0.474
3-Hexen-1-ol 5.139 14.851
Tilia honey Variables
Function 1 100%
Function 2
Conductivity 0.758
2-Butanone 1.036
3-Methyl-1-butanol -2.120
Hotrienol 1.827
The classification results (expressed as percentages) of the discriminant analysis carried
out by cross validated procedure demonstrated a very good classification of the acacia
and tilia honeys according to their country (Table 4). This was also true of sunflower
honey from Spain and Romania. However, 10% of sunflower honey from the Czech
Republic was incorrectly classified as sunflower honey from Romania.
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
122
Table 4. Classification results of the discriminant analysis carried out by cross validated procedure. Percentage of samples well classified by the model. Spain (Sp), Romania (Ro), and Czech Republic (Cz).
4. Conclusion
The information obtained about physicochemical parameters and volatile compounds is
a useful complement to that provided by the percentage of pollen to distinguish acacia,
sunflower and tilia monofloral honeys, with subsequent benefits for beekeepers and the
industry. Although it was found that the country (Spain, Romania, and the Czech Republic)
may lead to significant variations in the levels of certain parameters and compounds, it is
the type of honey that has by far the greatest influence on the differentiation of honeys,
above all due to the presence of certain volatile compounds such as carvacrol and -
terpinene in the case of tilia honey, -pinene and 3-methyl-2-butanol in sunflower honey,
and cis-linalool oxide in acacia honey. Discriminant models obtained for each kind of
botanical honey confirmed that the differentiation of honeys according to their country
of origin was principally based on volatile compounds (2-methyl-2-butenal, 2-methyl-2-
propanol, acetic acid butyl ester, etc., for acacia honeys; 1-hexanol, -pinene, etc., for
sunflower honeys and 3-methyl-1-butanol, hotrienol, 2-butanone, etc., for tilia honey)
and to a lesser extent on certain physicochemical parameters such as, diastase, sucrose
and conductivity, respectively.
A correct classification of all the samples was achieved with the exception of 10% of the
sunflower honeys from the Czech Republic. The main advantage of the model presented
Predicted Group Membership
Floral and Country origin
Acacia Sp
Acacia Ro
Acacia Cz
Sunflower Sp
Sunflower Ro
Sunflower Cz
Tilia Ro
Tilia Cz
Acacia Sp 100 0 0 - - - - -
Acacia Ro 0 100 0 - - - - -
Acacia Cz 0 0 100 - - - - -
Sunflower Sp - - - 100 0 0 - -
Sunflower Ro - - - 0 100 0 - -
Sunflower Cz - - - 0 10 90 - -
Tilia Ro - - - - - - 100 0
Tilia Cz - - - - - - 0 100
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
123
is to support the classification of the acacia, sunflower and tilia honeys according to the
country of origin. In order to be totally conclusive, it would be advisable to check the
predictive capacity of the proposed classification model with additional batches with the
same botanical and country origin but from different years.
5. References
Acquarone C., Buer, P. & Elizalde B. (2007). Pattern of pH and electrical conductivity upon
honey dilution as a complementary tool for discriminating geographical origin of
honeys. Food Chemistry, 101, 695-703.
Al M.L., Daniel D., Moise A., Bobis O., Laslo L., & Bogdanov S. (2009). Physico-chemical
and bioactive properties of different floral origin honeys from Romania. Food
Chemistry. 112 (4), 863–867.
Aliferis K.A., Ttarantilis P.A., Harizanis P.C., & Alissandrakis E. (2010). Botanical
discrimination and classification of honey samples applying gas
chromatography/mass spectrometry fingerprinting of headspace volatile
compounds. Food Chemistry, 121, 856-862.
Alissandrakis E., Tarantilis P.A, Harizanis P.C. & Polissiou M. (2005). Evaluation of four
isolation techniques for honey aroma compounds. Journal of the Science of Food
and Agriculture, 85, 91-97.
Anklam E., (1998). A review of the analytical methods to determine the geographical and
botanical origin of honey. Food Chemistry, 63, 549-562.
Bianchi F., Careri M. & Musci M. (2005). Volatile norisoprenoids as markers of botanical
origin of Sardinian strawberry-tree (Arbutus unedo L.) honey: characterisation of
aroma compounds by dynamic headspace extraction and gas chromatography–
mass spectrometry. Food Chemistry, 89, 527–535.
Bogdanov S., Martin P. & Lüllman C. (1997). Harmonised methods of the European Honey
Commission. Apidologie, Extra Issue, 1–59.
Bogdanov S. (2002). Harmonized methods of the International Honey Commission. Swiss
Bee Research Centre, FAM, Liebefeld, CH 3003 Bern, Switzerland.
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
124
Castro-Vázquez L., Diaz-Maroto M. C., González-Viñas M. A. & Pérez-Coello M. S. (2009).
Differentiation on monofloral citrus, rosemary, eucalyptus, lavender, thyme and
heather honeys based on volatile composition and sensory descriptive analysis.
Food Chemistry, 112(4), 1022-1030.
Council Directive 2001/110 relating to honey (2002). Official Journal of the European
Communities, 12.1.2002.
Cuevas-Glory L.F., Pino J.A., Santiago L. S., & Sauri-Duch E. (2007). A review of volatile
analytical methods for determining the botanical origin of honey.Food Chemistry,
103, 1032–1043.
De la Fuente E., Martinez-Castro I. & Sanz J. (2005). Characterization of Spanish unifloral
honeys by solid phase microextraction and gas chromatography-mass
spectrometry. Journal of Separation Science 28, 1093-1100.
Escriche I., Kadar M., Juan-Borrás M. & Domenech E. (2011). Using flavonoids, phenolic
compounds and headspace volatile profile for botanical authentication of lemon
and orange honeys. Food Research International, 44 (5), 1504-1513.
Fallico B., Arena E., Verzera A. & Zappalà M. (2006). The European Food Legislation and
its impact on honey sector. Accreditation and Quality Assurance11, 49-54.
Feás X., Pires J., Iglesias A. & Estevinho M.L. (2010a). Characterization of artisanal honey
produce don the Nirthwest of Portugal by melissopalynological and physic-chemical
data. Food and Chemical Toxicology, 48, 3462-3470.
Feás X., Pires J., Estevinho M.L., Iglesias A., & Pinto de Araujo J.P. (2010b). Palynological
and physicochemical data characterisation of honeys produced in the Entre-Douro
e Minho region of Portugal. International Journal of Food Science & Technology,
45, 1255-1262.
Gómez-Pajuelo A. (2004). Origen botánico de la miel. In Montagud S.A (Eds), Mieles de
España y Portugal (pp. 50-56). Barcelona, Spain.
Hutchings J.B. (1999). Food colour and appearance. Gaithersburg, Maryland (USA).
Kádár M., Juan-Borrás M., Hellebrandova E., Doménech& I. Escriche. (2010).
Differentiation of acacia, sunflower and tilia honeys from different countries based
on sugar composition, physicochemical and color parameters. Bulletin of University
of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 67(2), 252-258.
Kaskonienè V., Venskutonis P.R. & Ceksterytè V. (2010). Carbohydrate composition and
electrical conductivity of different origin honeys from Lithuania. LWT-Food Science
and Technology, 43, 801-807.
Kondjoyan N. & Berdagué J.L. (1996). A compilation of relative retention indices for the
analysis of aromatic compounds. Laboratoire Flaveur (INRA), Theix, France.
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
125
Kropf U., Korosec M., Bertoncelj J., Ogrinc N., Necemer M. & Kump P. (2010).
Determination of the geographical origin of Slovenian black locust, lime and
chestnut honey. Food Chemistry, 121, 839–846.
Lusic D., Koprivnjak O., Curic D., Sabatini A. G. & Conte L. S. (2007). Volatile profile of
Croatian lime tree (Tilia sp.), fir honeydew (Abies alba) and sage (Salvia officinalis)
honey. Food Technology and Biotechnology, 45(2), 156–165.
Mateo R. & Bosch-Reig F. (1998). Classification of Spanish unifloral honeys by discriminant
analysis of electrical conductivity, color, water content, sugars and pH. Journal of
Agricultural and Food Chemistry, 46, 393-400.
Oroian M. (2012). Physicochemical and Rheological Properties of Romanian Honeys. Food
Biophysics. 7, 296-307.
Oroian M., Amariei S., Escriche I. & Gutt G. (2013). Rheological Aspects of Spanish Honeys.
Food Bioprocess Technology, 6, 228–241.
Nozal M. J., Bernal J. L., Toribio L., Alamo M., Diego J. C. & Tapia J. (2005). The use of
carbohydrate profiles and chemometrics in the characterization of natural honeys
of identical geographical origin. Journal of Agricultural and Food Chemistry, 53,
3095-3100.
Piazza M.G. & Persano-Oddo L. (2004). Bibliographical review of the main European
unifloral honeys. Apidologie,35 (1), 94-111.
Persano-Oddo L., Piazza M. G., Sabatini A. G. & Accorti M. (1995). Characterization of
unifloral honeys. Apidologie, 26, 453–465.
Persano-Oddo L. & Bogdanov S. (2004). Determination of honey botanical origin:
problems and issues. Apidologie, 35, 2–3.
Persano-Oddo L. & Piro R. (2004). Main European unifloral honeys: descriptive sheets.
Apidologie35, 38–81.
Plutowska B., Chmiel T., Dymerski T. & Wardencki W. (2011). A headspace solid-phase
microextraction method development and its application in the determination of
volatiles in honeys by gas chromatography. Food Chemistry, 126, 1288-1298.
Radovic B. S., Careri M., Manglia A., Musci M., Gerboles M. & Anklam E. (2001).
Contribution of dynamic headspace GC–MS analysis of aroma compounds to
authenticity testing of honey. Food Chemistry, 72(4), 511–520.
Ruoff K., Luginbuehl W., Bogdanov S., Bosset J.O., Esterman B. & Ziorko T. (2007).
Quantitative determination of physical and chemical measurands in honey by near-
infrared spectrometry. European Food Research and Technology, 225, 415–423.
Saenz-Laín C. & Gómez-Ferreras C. (2000). Mieles españolas: Características e
identificación mediante el análisis del polen. España: Mundi-Prensa.
Effect of country origin on physicochemical, sugar and volatile composition of acacia, sunflower and tilia honeys
126
Serra Bonvehí J. & Ventura-Coll F. (2003). Flavour index aroma profiles of fresh and
processed honeys. Journal of the Science of Food and Agriculture, 83, 275–282.
Soria A. C., Gonzalez M., de Lorenzo C., Martinez-Castro I. & Sanz J. (2004).
Characterisation of artisanal honeys from Madrid (Central Spain) on the basis of
their melissopalynological, physicochemical and volatile composition data. Food
Chemistry, 85, 121-130.
Terrab A., Gustavo-González A., Díez M. J. & Heredia F.J. (2003). Characterization of
Moroccan unifloral honeys using multivariate analysis. European Food Research
and Technology, 218, 88-95.
Visquert M., Vargas M. & Escriche I. (2013). Effect of postharvest storage conditions on
the colour and freshness parameters of raw honey. International Journal of Food
Science and Technology, 49(1), 181-187.
Von Der Ohe W., Persano-Oddo L., Piana M. L., Morlot M. & Martin P. (2004). Harmonized
methods of melissopalynology. Apidologie, 35, 18-25.
Marisol Juan-Borrás, Angela Periche, Eva Domenech, Isabel Escriche
Food Control, 50, 243-249 (2015)
3.5. Routine quality control in honey packaging companies as a key to
guarantee consumer safety. The case of the presence of sulfonamides
analyzed with LC-MS-MS.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
129
Abstract
One of the main challenges in the Horizon 2020 framework is to ensure sufficient food
and feed, while monitoring safety throughout the food chain. In this context, the
objective of this paper was to evaluate the efficacy of the routine quality control that
honey companies carry out on raw batches (before entering the industrial packaging
process) considering the presence of sulfonamides. A total of 279 honey samples were
analyzed in this study: 178 raw honey samples were taken on reception in different
companies, and 101 samples (from the same industries) were purchased locally. The
validation of the methodology applied (LC–MS/MS) before analyzing the samples,
confirm the reliability of the results obtained. All the purchased samples were found to
be negative for sulfonamides, however, in 9 raw samples sulfathiazole (6 samples) and
sulfadiazine (3 samples) were found, which represents 3.4% and 1.7 % of the 178 raw
samples analysed, respectively. Therefore, if monitoring is carried out routinely at
reception, risk can be decreased to a negligible level. The results confirm that using a
suitable analytical methodology and implementing an appropriate routine quality control
on reception is totally effective to avoid the presence of sulfonamides in the
commercialized product, thereby ensuring consumer safety.
Keywords: sulfonamides; honey; LC-MS-MS; consumer safety
1. Introduction
Honey is a very healthy, nutritious food, however, in recent years it has been the focus of
food alerts due to the presence of chemical hazards such as antibiotics or pesticides. The
origin of these residues in honey is mainly veterinary treatments (acaricides,
sulfonamides, antibiotics, etc.) required to treat bee parasites and bacterial diseases such
as European foulbrood (Streptococcus pluton) or American foulbrood (Bacillus larvae)
which can destroy an apiary, and propagate to other bee-hives very easily; although these
compounds are often used in bee-keeping as preventive or therapeutic treatments to
protect an apiary (Staub-Spörri et al., 2014).
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
130
Chemical hazards has become a major concern for the administration and the honey
sector due to both the important consequences for public health (allergic reactions,
bacterial resistance, changes in intestinal flora, etc.), and the impact on bees. In fact, the
European Commission states that if a food-producing animal has to be treated with
medicines to prevent or cure disease, the veterinary residues in these food products
should not harm the consumer (European Commission, 2007). In the new societal
challenges proposed by The EU Framework Programme for Research and Innovation,
Horizon 2020 (Commission Decision, 2014), meeting consumer needs and preferences,
but minimising the related impact on health and the environment is included as one of
the main goals. The point “Food security, sustainable agriculture and forestry, marine,
maritime and inland water research, and the bioeconomy” highlights that research
should address food and feed safety, covering the whole food chain and related services
from primary production to consumption. In truth, the control of all stages of the food
chain «from farm to fork» is a shared responsibility, including primary production
(agricultural and livestock), and industrial processing. It is essential to ensure consumer
protection, the last link of the chain.
In order to minimize consumer exposure to residues, the Commission requires EU
countries to implement residue monitoring plans through official control to monitor the
illegal use of substances and misuse of authorized veterinary medicines (Commission
Decision C 4995, 2014). Thus, Council Directive 96/23/EC (1996) and Commission
Decision 97/747/EC (1997)establish the frequency of sampling and the levels of the
groups of substances to be monitored, considering veterinary medicines, pesticides and
contaminants in food of animal origin. This situation calls for the development of a
quantitative framework based on risk assessment (the tool for science-based decision-
making) to estimate the impact on health, and to increase the efficiency and effectiveness
of safety evaluations.
Bearing all of this in mind, the objective of the current study was to evaluate the
effectiveness of the routine quality control sampling which companies carry out on raw
batches of honey (before entering the industrial packaging process) considering the
presence of sulfonamides. To this end, both raw samples (unprocessed honey collected
randomly from the initial stage of the different industries) and commercialized samples
(from the same industries but bought locally) were evaluated. Before analyzing the
samples, the methodology applied (LC–MS/MS) was developed and validated to
guarantee the reliability of the results. As a first step in the validation process, the matrix
effect of the proposed method was studied.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
131
2. Materials and Methods
2.1.-Chemicals and Reagents
Sulfanilamide, sulfathiazole, sulfamerazine, sulfadiazine, sulfapyridine, sulfamethazine,
sulfamethizole, sulfachloropyridazine, sulfamethoxazole, sulfadimethoxine, and
sulfaquinoxaline; where purchased from Sigma (Steinheim, Germany), with a purity ≥95%
in all cases. Hydrochloric acid (37%), formic acid (FA, 99%), acetonitrile (ACN) and
methanol (MeOH) were obtained from Prolabo (VWR, Fontenay-sous-Bois, France);
ammonia solution was purchased from Sharlau (Barcelona, Spain) and citric acid
monohydrate was acquired from Merck (Darmstadt, Germany). The solid phase
extraction (SPE) columns Strata X-CW (33µm, 100 mg, 3mL) were obtained from
Phenomenex (Torrance, CA). Ultrapure water was generated in-house from a Milli-Q
system (Millipore Corp., Billerica, MA).All reagents were MS, HPLC or analytical grade.
Individual stock solutions of all standards were prepared in methanol at a concentration
of 1mg/mL and stored in a freezer at -20ºC, the concentrations were corrected for purity
and salt form. The stock solutions were stable for at least 6 months (Kaufmann et al.,
2002). A working standard mix solution of the 11 sulfonamides, in a concentration of
1g/mL, was prepared in water. This solution was used to construct the calibration curves
and to prepare the spiking experiments. Before each use it was left to reach room
temperature. The stability of the 11 sulfonamides in the mixed working standard solution
was checked to ensure that the standard could be stored at +4ºC for at least 3 months,
with no decrease in response or degradation.
2.2.-Honey samples
A total of 279 multifloral honey samples from the Valencian Region (Spain) were used in
this study. 178 of them were taken from the routine quality control sampling which
companies carry out on every batch of raw honey before entering the industrial
packaging process. The other 101 samples (from the same industries), were purchased
locally. All the samples were stored in a dark, dry place at room temperature until
analysis.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
132
A mixture of 10 multifloral honeys without the compounds analyzed in this study was
selected as a “blank honey” in order to perform the validation procedure of the
methodology. Multifloral honeys with very different physicochemical characteristics
(colour and texture) were specifically selected in order to cover the widest range of
variability, using both light and dark honeys. This is a common procedure used by
different authors to obtain a blank honey (Hammel et al., 2008; Martinez Vidal, et al.,
2009; Dubreil-Chéneau et al., 2014). It is important to point out that our experience on
honey analysis, as well as the results observed by other authors, showed that, in general,
the types of honey don’t affect the accuracy of the method (Dubreil-Chéneau et al.,
2014). Although in specific cases some modifications could occur to certain analyte
signals (ion suppression or enhancement) for particular types of honey, these differences
are less important than those due to the intrinsic inter-day variation of the method
(Dubreil-Chéneau et al., 2014). Notwithstanding this, in the case of very dark honeys, like
chestnut honeys, a matrix effect for some analytes could be observed (Galarini et al.,
2014). This may lead to the conclusion that for the specific case of very dark honeys it
would be advisable to use this same type of honey as a “blank honey”.
2.3.-Analytical determinations
2.3.1. Sulfonamide extraction method in honey
Samples of honey (1.0g) were placed in beaker flasks. The fortified samples were
prepared by adding the mixed working standard solution (1g/mL) to the blank honey to
obtain the appropriate levels for validation of the method. Then, they were shaken well
and allowed to stand for at least 1 hour to permit sufficient absorption of the different
standards. After addition of 1mL 0.1M HCl, the samples were dissolved using a magnetic
stirrer and left to stand at room temperature for at least 20 minutes to allow hydrolization
of the sulfonamides (80-90% of sulfonamides are bound to sugars). Then, 5 mL of 3M
citric acid were added and stirred for 30 s. Next, 5mL of the honey solution was passed
through the SPE column, previously conditioned with 3mL of MeOH and 3mL of ultrapure
water. The cartridges were then washed by adding 3 mL MeOH/ACN (1/1) twice. The
cartridges were vacuum drained, by passing air through them, for 2 min at a pressure of
10 mmHg. The elution was accomplished with 3 mL of 2% ammonium hydroxide in
MeOH, and the analytes were collected in 6mL glass tubes. The SPE procedure was
performed in a Lichrolut vacuum manifold coupled to a vacuum pump (Merck,
Darmstadt, Germany). Finally, the eluates were evaporated to dryness under a stream of
nitrogen while being maintained at 40 ºC in a thermostatic bath (Grant GR, Cambridge,
England). After evaporation, 100 µL of mobile phase was added to each tube, and
thoroughly mixed to ensure the complete dissolution of the extract. Finally, the re-
dissolved extracts were injected into the LC-MS/MS system.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
133
2.3.2. LC/MS/MS Analysis
The chromatography system consisted of a HPLC Agilent 1200 Infinity Series coupled to
an Agilent 6420 Triple Quadrupole detector, equipped with a source set in positive
electrospray ionization mode. The column used was a Zorbax Eclipse XDB-98 (4.6 x 50
mm, 1.8 microns) supplied from Agilent. Chromatographic separation was carried out
with a mobile phase consisting of 0.5% formic acid in water (mobile phase A) and ACN
(mobile phase B) with a flow rate of 0.4 mL/min. The gradient used started with mobile
phase A at 20%, then at minute 4 was 30%, reaching 40% at minute 7. These conditions
were maintained until minute 9. After that, the system was left for 4 min to re-equilibrate
before the next injection. The oven column was set at 30 ºC, and the injection volume
was 5 L.
The system was equilibrated at the beginning of each day for 1h, and three injections of
the standard solution were made to check its stability and the response of the equipment,
a solvent blank was then injected to assess the cross-talk. The operating parameters for
the mass spectrometer were as follows: capillary voltage 4 kV; source temperature 350
ºC; nebulization gas (nitrogen) at a flow rate of 12 L/min and collision gas (nitrogen) at a
flow rate of 3 L/min and 40 psi. The optimization of the MS/MS operating parameters
were performed by the automatic optimization function of the MS software (Optimizer,
Agilent), using direct infusion, without column, of the mixed working standard solution of
the 11 sulfonamides, at a concentration of 40g/L. The most important LC-ESI-MS
parameters for the acquisition and identification of the 11 target compounds are
summarized in Table 1.
Table 1.- MS/MS operating parameters used in Sulfonamide analysis.
Analyte Quantification
Transition Confirmation
Transition Fragmentor CEa(V)
RTb (min)
Sulfanilamide 173.0>93.1 173.0>156.1 100 5/15 1.78 Sulfathiazole 256.0>156.1 256.0>92.1 91 8/24 2.36 Sulfadiazine 251.5>156.0 251.5>92.1 91 12/24 2.45 Sulfapyridine 250.1>156.1 250.1>92.1 121 12/28 2.55 Sulfamerazine 265.1>172.1 265.1>92.1 121 12/28 2.95 Sulfamethazine 279.1>186.1 279.1>124.1 121 12/24 3.40 Sulfamethizole 271.0>156.1 271.0>92.1 120 8/24 3.59 Sulfachloropyridazine 285.0>156.0 285.0>92.1 91 8/24 5.60 Sulfamethoxazole 254.0>156.1 254.0>108.1 91 12/24 6.50 Sulfadimethoxine 311.1>156.1 311.1>124.1 121 16/32 8.60 Sulfaquinoxaline 301.1>156.0 301.1>108.1 121 12/24 8.70
a =collision energy; b = retention time
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
134
2.3.3. Matrix effect evaluation and quantification
Food matrices can vary in terms of complexity and content, and it is well established that
co-eluting matrix constituents may interfere with the ionization process of the analytes
(Sterner et al., 2000; Lopez et al., 2008). In order to evaluate the matrix effect, the
calibration curve in solvent should be compared with the calibration curve in matrix, and
a quantitative measure of the ion suppression or enhancement can be obtained
comparing the peak areas of the analyte standards in solvent and the peak areas of the
analyte standards spiked in honey before extraction.
The calibration method of standard addition was used to quantify the sulfonamides.
Therefore, a 7-point standard curve (including zero) was constructed for each
sulfonamide by plotting the peak area of the SRM transitions showing the most intense
signal of each analyte versus its nominal concentration. As honey has no MRLs (maximum
residue levels) for these compounds, the European Commission (Regulation (EC) No
470/2009) states that if sulfonamides are present, they must be below the limit of
quantitation according to the analytical method used. Due to the fact that this limit differs
between laboratories and that there is no legislation or official recommendation, in this
study the target limit considered was 10 g/kg, as this is the most demanding action limit
or tolerated level found in the literature in Europe (Muñoz de la Peña et al., 2007; Sajid
et al., 2013). In addition to this, a further lower level of 5g/kg was considered in order
to evaluate values lower than the target limit.
Therefore, to construct the curves, the blank honey was fortified with 0, 5, 10, 20, 40, 60
and 100 g/kg of each compound, injected in duplicate into the LC-MS/MS system. This
process was carried out in triplicate.
To identify a sample as positive, three criteria were considered: first, the signal-to-noise
ratio (S/N) of the product ions selected must be greater than or equal to three; second,
the allowable deviation of the retention time of the target matter and that of its
corresponding standard should be within ±2.5%; third, the allowable deviation of the
relative abundance of the characteristic ions of the target matter and those of the
characteristic ions corresponding standard should be within ±20%.
2.3.4. Validation of the sulfonamide analytical method in honey
The analytical methodology applied in this work was validated as a first step in order to
ensure the reliability of the results for every compound in the quantification range
considered. The present validation study was performed in accordance with Commission
Decision 2002/657/EC (2002). To this end several parameters were studied: selectivity,
linearity, recovery, precision (repeatability or intraday precision “RSDr” and
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
135
reproducibility or interday precision “RSDR”), accuracy, decision limit (CCα) and detection
capability (CCβ).
The selectivity, or ability of the method to differentiate and quantify each analyte in the
presence of potentially interfering substances in honey samples, was evaluated by
analyzing the blank honey 20 times. To this end, the absence of any interference in the
segment of the retention window of each product ion was verified for each analyte.
Linearity (R2) was tested in the 0-100 g/kg range drawing seven-point calibration curves
for fortified honey blanks. The accuracy of this method was evaluated through recovery
experiments, carried out by spiking a honey blank with aliquots of the mixed working
standard solution before the extraction procedure to obtain the seven concentration
levels (0, 5, 10, 20, 40, 60 and 100 g/kg). Six replicates were performed at each level.
Recoveries for each analyte were determined by comparing the concentrations obtained
from the calibration curves for the fortified blanks with their nominal concentrations.
Recovery was measured as a percentage and RSD. The precision of the developed method
was evaluated in two stages: intra-day precision (RSDr) and inter-day precision (RSDR). To
determine intraday precision six samples per level were spiked just before analysis and
extracted on the same day, at the same levels as for recovery. The experiment was
repeated on two further days to determine inter-day precision. Intra-day precision was
expressed as the RSD of samples extracted the same day, at the same concentration,
inter-day was expressed as the RSD of samples extracted on different days, at the same
concentration.
CCα (decision limit) was carried out by analyzing the blank honey 20 times and calculating
the signal to noise ratio at the time window in which each analyte was expected. Three
times the signal to noise ratio was used as the decision limit. CC(detection capability)
was determined by analyzing the blank honey fortified with the analytes at the decision
limit at least 20 times. Detection capability is equal to the value of the decision limit plus
1.64 times the corresponding standard deviation of the within-laboratory reproducibility.
With the strategy described above, linearity, recovery, and precision were determined
through 42 measurements.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
136
3. Results and Discussion
3.1.-Matrix effect
LC-MS/MS detection is considered to be the best tool for good selectivity and speed of
analysis (Cirić et al., 2012). However, it should be taken into account that the results may
be adversely affected by lack of selectivity (Rogatsky & Stein, 2005) due to ion
suppressions or ion enhancement caused by the sample matrix, interferences from
metabolites, and “cross-talk” effects (Matuszewski et al., 2003). Because the matrix effect
may compromise the quantitative results as well as the reproducibility of the method, as
a first step in the validation process, the matrix effect (ME) of the proposed method was
carefully studied and calculated as described in Eq.1. If ME(%)=100, no matrix effect is
present; if ME(%)>100 there is a signal enhancement and if ME(%)<100 there is a signal
suppression.
𝑀𝐸 % = 𝑝𝑒𝑎𝑘 𝑎𝑟𝑒𝑎 𝑜𝑓 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑖𝑛 𝑠𝑜𝑙𝑣𝑒𝑛𝑡
𝑝𝑒𝑎𝑘 𝑎𝑟𝑒𝑎 𝑜𝑓 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑠𝑝𝑖𝑘𝑒𝑑 𝑏𝑒𝑓𝑜𝑟𝑒 𝑒𝑥𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛 × 100 (1)
Three nominal concentration levels were considered to calculate the matrix effect: low-
level=5 g/kg, medium level= 20 g/kg and high level= 40 g/kg (Table 2) (Sajid et al.,
2013). All the analytes showed a signal enhancement for the 3 concentration levels to a
greater or lesser extent. Sulfanilamide was the least affected by the matrix effect because
ME was 100% at the highest concentration level, and very close to it at the lowest and
medium concentration levels (113 and 114, respectively). On the contrary,
sulfamethoxazole with values of 463, 354 and 316, showed the most marked signal
enhancement effect.
To estimate the matrix effect it is also possible to compare the slopes of calibration plots
built both for the standards in methanol solution and for the standards additions in blank
honey samples, which is more visual (Taylor, 2005; Gosetti et al., 2010). As an example,
Figure 1 shows these calibration curves obtained for sulfanilamide and sulfamethoxazole.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
137
Table 2. Matrix effect of the 11 sulfonamides studied.
Nominal Concentration
Low-level:
5g/kg
Medium-level:
20 g/kg
High-level:
40 g/kg
Sulfanilamide 113 114 100 Sulfathiazole 179 206 185 Sulfadiazine 183 173 181 Sulfapyridine 212 168 152 Sulfamerazine 294 262 242 Sulfamethazine 340 278 270 Sulfamethizole 329 313 340 Sulfachloropyridazine 335 322 323 Sulfamethoxazole 463 354 316 Sulfadimethoxine 398 349 241 Sulfaquinoxaline 209 149 135
Due to the fact that an enhancement phenomenon was observed in this study for the
eleven sulfonamides studied, it was decided to carry out the quantification step using the
standard addition method (that is to say, quantification based on matrix-spiked
calibration solution). In this way, the matrix effect was efficiently minimized (Economoui
et al., 2012).
Figure 1. Calibration curves obtained for sulfanilamide (A) and sulfamethoxazole (B) in methanol
solution (white circles) and in standard additions in blank honey sample (asterisks).
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
138
3.2.-In-house validation method
The analytical methodology used to perform the sulfonamide analyses of the honey
samples was subjected to an in-house validation method. The selectivity, as mentioned
before, was evaluated by comparing 20 chromatograms obtained from the analysis of the
corresponding blank honey sample and those obtained from blank honey fortified with
11 sulfonamides. Figure 2 shows as an example the selected reaction monitoring (SRM)
chromatogram of a blank honey and the same honey fortified at 20 g/kg with all the
sulfonamides studied. The absence of interference, which could compromise the
identification and quantitation of the analytes, was verified near the retention time of
each sulfonamide. Regarding the linearity, the results demonstrated that in the range
studied 5-100 g/kg, the method showed a good linearity for all the sulfonamides, with
a linear coefficient between 0.993 and 0.999. This is considered adequate according to
the recommendations of regulatory agencies such as the Commission Decision
2002/657/EC.
Figure 2. Selected reaction monitoring (SRM) chromatogram of a blank honey extract (A) and the
same honey fortified at 20 g/kg (B) with all the sulfonamides studied.(1)Sulfanilamide; (2) Sulfathiazole; (3) Sulfadiazine; (4) Sulfapyridine; (5) Sulfamerazine; (6) Sulfamethazine; (7) Sulfamethizole; (8) Sulfachloropyridazine; (9) Sulfamethoxazole; (10) Sulfadimethoxine; (11) Sulfaquinoxaline; (M) Matrix.
The data about the other validation parameters are shown in Table 3. These parameters
provide information regarding the recovery, precision (repeatability or intra-day
precision RSDr and reproducibility or inter-day precision RSDR), decision limit (CCα) and
detection capability (CCβ)The recoveries of all sulfonamides were in a range of 89-114%,
complying with the requirements of the Commission Decision 2002/657/EC, which
concludes that the proposed method shows good accuracy for all the studied analytes.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
139
The repeatability (RSDr) for all the sulfonamides studied ranged from 3.0 to 19.5%, in
agreement with this Commission Decision. In the case of reproducibility (RSDR), for 6 of
the 11 compounds this parameter was below the required value: lower than 20%. In a
few cases for the other four compounds, this parameter was exceeded slightly but its
value was very close to 20% (always lower than 24%): Sulfanilamide at the 10 μg/kg level
showed 22.8 %; sulfamerazine at the 20 μg/kg level showed 22.3%; sulfachloropiridazine
20.8% at the 10 μg/kg level, sulfamethazine at the 10 and the 20 μg/kg levels showed
23.7 and 22.1% and sulfaquinoxaline 22.7 and 22.9% at the 20 and the 40μg/kg level
respectively. Therefore it can be concluded that the method used has good precision
(repeatability and reproducibility) (Bohm et al., 2013).
The limit of decision (CC) values ranged from 0.7 g/kg (sulfamethoxazole) to 4.5 g/kg
(sulfamethazine) and the detection capability (CC) limit from 2.3g/kg
(sulfamethoxazole) to 4.3 g/kg (sulfadiazine). It is noticeable that in all the cases the 2
limits are below 5μg/kg, which is the target minimum level in this paper, as mentioned
before. The results of the validation demonstrate that the applied analytical procedure
guarantees the quantitative values of sulfonamides in the samples analyzed.
Table 3. Validation parameters for the analytical method.
Analytes Nivel (μg/kg)
Recovery % RSDra % RSDR
b % CCα (μg/kg)
CCβ (μg/kg)
Sulfanilamide 5 10 20 40 60
100
98 105 95 89
104 104
15.3 16.9 10.5 3.5
10.9 7.3
12.4 22.8 17.8 16.5 14.8 9.0
1.5 3.1
Sulfathiazole 5 10 20 40 60
100
114 108 96 95 93 98
4.6 4.5 7.2 8.6 7.3 8.5
14.2 11.6 14.0 16.6 14.6 10.3
2.4 4.0
Sulfadiazine 5 10 20 40 60
100
104 106 97 94 98
104
9.9 13.8 10.8 3.5 5.1 6.0
8.9 14.8 18.4 11.9 7.2 8.1
2.8 4.3
Sulfapyridine 5 10 20 40 60
100
101 104 98 99 95
102
6.2 6.1 7.0 3.9 5.6 4.6
9.6 18.9 18.1 15.4 7.3
13.3
2.2 3.0
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
140
Table 3 (Cont)
Analytes Nivel (μg/kg)
Recovery % RSDra % RSDR
b % CCα (μg/kg)
CCβ (μg/kg)
Sulfamerazine 5 10 20 40 60
100
109 101 96 89 97
101
7.4 4.5 9.7 8.8 6.3 6.6
6.8 19.5 22.3 15.0 10.1 14.1
1.4 3.0
Sulfamethazine 5 10 20 40 60
100
93 110 96 99 95
101
7.5 6.3
10.5 9.2 6.6 6.3
16.3 23.7 22.1 17.8 8.1
17.7
4.5 4.1
Sulfamethizole 5 10 20 40 60
100
99 101 96 97 98
102
5.7 6.8 6.5 9.6
16.9 4.5
12.5 8.1
13.5 15.2 17.1 7.4
2.0 3.0
Sulfachloropyridazine 5 10 20 40 60
100
109 101 95 97 98
101
10.3 6.3 7.3 8.8 8.8 4.9
11.5 20.8 18.7 17.0 11.9 16.3
2.0 3.5
Sulfamethoxazole 5 10 20 40 60
100
101 101 95 93 99
101
11.3 8.6
10.2 6.5 6.6 5.4
11.5 16.3 19.3 10.5 10.3 10.0
0.7 2.3
Sulfadimethoxine 5 10 20 40 60
100
94 98 97 99
100 101
8.6 9.4
11.0 3.0 6.9 3.2
11.6 17.2 19.9 13.9 9.9
15.7
2.0 3.5
Sulfaquinoxaline 5 10 20 40 60
100
89 93 96
100 98 99
6.4 8.5 8.8
12.6 19.5 7.7
12.2 19.4 22.7 22.9 16.4 17.9
2.4 3.9
aRSDr= repeteatability; bRSDR = reproducibility
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
141
3.3.-Samples analyses
Of the 279 honey samples analysed for the presence of 11 sulfonamides, 64% of them
were from the routine sampling of every batch of raw honey which companies realize
before the industrial packaging process and the other 36% samples (from the same
industries), were purchased in local shops. The values of the percentage of positive
samples and quantitative results of the sulfonamides found are shown inTable 4. The
sulfonamide levels reported are the mean of three replicates obtained by subjecting the
sample to the extraction and the analysis process. This was done to confirm that the
results were not derived from incidental sample contamination. All the purchased
samples had a “negative result” for all the sulfonamides, which means that the values
obtained were under the CC. On the contrary, in 9 raw samples sulfathiazole (6 samples)
and sulfadiazine (3 samples) were found, which represents 3.4 % and 1.7 % of the 178
raw samples analysed, respectively. For sulfathiazole the levels ranged between 5 and 9
g/kg, whereas for sulfadiazine a minimum of 13 µg/kg was found and a maximum that
exceeded the maximum limit of quantification (100 µg/kg).
Table 4. Samples analyzed, percentage of positives for sulfonamides and concentration range.
Honey samples purchased locally
Raw honey samples from the routine quality control
in companies
Concentration range (µg/kg) of the positive
compounds
Nº of analysed samples 101 178
Nº of positive samples 0 9
% of samples positive for sulfathiazole
0 3.4 5-9
% of samples positive for sulfadiazine
0 1.7 13-(100≤)
The “positive” samples found in the present work on raw honey are clearly in violation of
current European directives. Although in the Commission regulation (Commission
Decision 2010/37/EC) for the establishment of maximum residue limits of veterinary
medicinal products in foodstuffs of animal origin MRLs are not included for honey, some
EU countries have established internal MRLs for some substances. In the case of
sulfonamides (based on the sum of sulfonamide family), the permitted level ranges from
20 g/kg in Belgium to 50g/kg in the UK (Maudens et al., 2004). There is an obvious
discrepancy between countries which affects commercial transactions. At present the
limit for sulfonamides and other antibiotics in honey is established taking into account
the limit of quantification of the methodology used. For instance, 10 g/kg for
sulfonamide in honey, a value which is only attained by techniques such as LC-MS/MS
(Sheridan et al., 2008; Martinez Vidal et al., 2009). In fact, the afore mentioned Council
regulation (Commission Decision 2010/37/EC) recognizes that it is becoming easier to
detect the presence of residues of veterinary medicines in foodstuffs (meat, fish, milk,
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
142
eggs and honey) at ever lower levels as a consequence of scientific and technical
progress.
In other studies carried out with a similar detection technique to that used in the present
work, sulfathiazole is also one of the most present sulfonamides in honey. For instance,
in 116 honey samples analyzed from Eastern Europe sulfathiazole was present in 47% of
the cases (Sheridan et. al, 2008). In a set of honey samples from the USA, Asia and Europe,
the presence of sulfadiazine, among other sulfonamides, and the presence of
sulfathiazole in 2 samples (Hammel et al., 2008). In the report “Monitoring of veterinary
medicinal product residues and other substances in live animals and animal products”
published by European Food Safety Authority about the results obtained in 2011 it is
noteworthy that the highest frequency of non-compliant samples for antibacterials
(including sulfonamides) was observed in honey (European Food Safety Authority (2013).
In relation to the specific case of sulfonamides the study mentions positive cases in 4 out
of 129 samples from Poland. Sulfadimethoxine was found in 2 out of 67 samples from
Hungary and sulfathiazole in 1 out of 5 from Lithuania. However, it is important to
mention that in some countries there are specific control programs, applied to different
live animals and animal products, which use microbiological tests (inhibitor tests), and
sometimes the positive results are not confirmed by the most appropriate technique and
thus there is no conclusive quantification of the substance concerned (European Food
Safety Authority, 2013).
More recent results were reported by Galarini et al. (2014) based on 74 honey samples
acquired in the Italian market. The samples had both different botanical and geographical
origins (such as Italy, Hungary, Argentina, Bulgaria, Romania, Spain, and other EU and non
EU countries). 12% of the samples analyzed by LC-MS/MS had traces of sulfonamides.
More specifically, in 5 samples concentrations between 0.3 to 1.7g/kg of sulfathiazole,
and in 4 samples concentrations between 0.2 to 1.7 g/kg of sulfadimethoxine were
confirmed. These authors pointed out that their results were in agreement with those
reported by the Italian National Reference Laboratory for Beekeeping. This laboratory
analyzed over 1500 honeys during a time period of six years, observing that 11% of the
samples contained sulfonamide residues.
In Spain the most recent data from the official monitoring of antibiotics in honey are
published by the Spanish Agency for Food Safety in the 2012 and 2013 reports (AESAN,
2012; AESAN 2013). In both years no antimicrobials were detected in more than 700
honey samples analyzed every year.
The above mentioned information shows that although the use of sulfonamides in
beekeeping is banned in the European Union, the occurrence of residues of these
compounds in honey samples is significant when sensitive analytical methods are used.
However, when these compounds are present in honey they occur at very low levels,
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
143
even lower than in the tissues of farm animals. Therefore, they are not important from a
toxicological point of view (Baran et al., 2011) given that honey is consumed in very small
quantities. In this context, it is possible to estimate the risk to the consumer associated
with the presence to this chemical hazard in honey.
This risk to the consumer is defined as a combination of the probability of occurrence of
a hazard and the severity of this hazard in terms of human health:
Risk=Probability*Severity (FAO/WHO 1995; Doménech et al., 2007). Asselt et al. (2013)
considered that this probability must be established as the probability of consumption
and the probability of exposure. They estimated the severity of a hazard associated with
an antibiotic residue as both the intrinsic toxicity of the antibiotic and the consequences
for human health related to the development of antimicrobial resistance. Taking this into
account, these authors attributed scores to the above mentioned factors for antibiotics
in different foods, including sulfonamides in honey, assigning a value in the range 0-3
(where 0 is low and 3 is high) for every factor. In the case of sulfonamides in honey, they
established a value of 1 for the severity. Considering this value and the results obtained
in the present paper, the risk to the consumer associated with eating commercial honey
samples is 0 because no positive samples were found. However, this value would be 1 if
the companies did not monitor the raw honey samples before the industrial packaging
process as 3.4% and 1.7% of the analyzed raw honey samples were positive for
sulfathiazole and sulfadiazine, respectively. This value concurs with the European Food
Safety Authority report (2013), which remarked that a real risk of exposure to different
sulfonamides in honey exists. Therefore, if adequate monitoring is carried out routinely
at reception, the risk can decrease from 1 to 0 in a range of 0 to 3. This highlights the
importance of the routine quality control that each company is expected to carry out.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
144
4. Conclusion
A quantitative LC-MS/MS method for the determination of 11 sulfonamides in honey was
validated with good results in agreement with the Commission Decision 2002/657/EC,
which confirm the results of sulfonamides obtained in the honey samples analyzed.
Monitoring the raw honey samples, before the industrial packaging process, showed that
a real risk to the consumer exists due to the presence of sulfonamides. However, the
results from honey sampled at retail confirmed that correct monitoring by the company
is able to reduce the risk to an acceptable level. To sum up, the results indicate that using
a suitable analytical methodology and implementing the routine quality control that each
company is expected to carry out, it is possible to avoid the presence of sulfonamides,
and therefore to ensure consumer safety. This can be extrapolated to other chemical
hazards that could be present in any kind of honey.
Acknowledgment
This study forms part of a project funded by the Ministerio de Educación y Ciencia of
Spain (Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los retos
de la sociedad; project number AGL2013-48646-R), for which the authors are grateful.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
145
5. References
AESAN (Agencia Española de Seguridad Alimentaria) (2012). Memoria 2012. Available:
http://aesan.msssi.gob.es/AESAN/docs/docs/publicaciones_estudios/memoria/memori
a_2012.pdf
AESAN (Agencia Española de Seguridad Alimentaria) (2013). Memoria 2013. Available at:
http://aesan.msssi.gob.es/AESAN/docs/docs/publicaciones_estudios/memoria/m
emoria_2013.pdf
Asselt E.D. van, Spiegel M. van der, Noordam M.Y., Pikkemaat M.G. & Fels-Klerx H.J. van
der. (2013). Risk ranking of chemical hazards in food. A case study on antibiotics in
the Netherlands. Food Research International, 54, 1636-1642.
Cirić A., Prosen H., Jelikić-Stankov M. & Durdevic P. (2012). Evaluation of matrix effect in
determination of some bioflavonoids in food samples by LC–MS/MS method.
Talanta, 99, 780-790.
Baran W., Adamek E., Ziemian J. & Sobczak A. (2011). Effects of the presence of
sulfonamides in the environment and their influence on human health. Journal of
Hazardous Materials, 196, 1-15.
Bohm D.A, Stachel C.S. & Gowick P. (2013). Development and validation of a confirmatory
method for the determination of tranquilisers and a β-blocker in porcine and
bovine kidney by LC-MS/MS. Food Additives & Contaminants,30 (6), 919-920.
Commission Decision 97/747/EC. (1997). Fixing the levels and frequencies of sampling
provided for by Council Directive 96/23/EC for the monitoring of certain substances
and residues thereof in certain animal products. Official Journal of European
Commission,L303/12 Available
at:http://ec.europa.eu/food/food/chemicalsafety/residues/comm_dec1997_747e
c.pdf
Commission Decision 2002/657/EC. (2002). Implementing Council Directive 96/23/EC
concerning the performance of analytical methods and the interpretation of
results. Official Journal of European Commission, L221, 8–36. Available at:
http://crl.fougeres.anses.fr/publicdoc/decision2002-657-EC
crit%C3%A8resvalidation.pdf
Commission Decision 2010/37/EC. (2010). On pharmacologically active substances and
their classification regarding maximum residue limits in foodstuffs of animal origin.
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
146
Official Journal of European Commission, L15/1. Available
at:http://ec.europa.eu/health/files/eudralex/vol/reg_2010_37/reg_2010_37_en.
Commission Decision C 4995. (2014). Horizon 2020 Work Programme 2014-2015. 9. Food
security, sustainable agriculture and forestry, marine and maritime and inland
water research and the bioeconomy. Available at:
http://ec.europa.eu/research/participants/data/ref/h2020/wp/2014_2015/main/
h2020-wp1415-intro_en.pdf
Council Directive 96/23/EC (1996). On measures to monitor certain substances and
residues thereof in live animals and animal products and repealing Directives
85/358/EEC and 86/469/EEC and Decisions 89/187/EEC and 91/664/EEC. L 125/10-
31. Available at:
http://ec.europa.eu/food/food/chemicalsafety/residues/council_directive_96_23
ec.pdf.
Domenech E., Escriche I. & Martorell S. (2007). Quantification of risks to consumers’
health and to company’s incomes due to failures in food safety. Food Control, 18,
1419-1427.
Dubreil-Chéneau E., Pirotais Y., Verdon E. & Hurtaud-Pessel D. (2014). Confirmation of 13
sulfonamides in honey by liquid chromatography-tandem mass spectrometry for
monitoring plans: Validation according to European Union Decision 2002/657/EC.
Journal of Chromatography A, 1339, 128–136.
Economou A., Petraki O., Tsipi D. & Botitsi E. (2012). Determination of a liquid
chromatography–tandem mass spectrometry method for the determination of
sulfonamides, trimethoprim and dapsone in honey and validation according to
Commission Decision 2002/657/EC for banned compounds. Talanta, 97, 32-41.
European Commission (2007). A new Animal Health Strategy for the European Union
(2007-2013) where “Prevention is better than cure”. Available at:
http://ec.europa.eu/food/animal/animalproducts/index_en.htm.Assessed April
2014.
European Food Safety Authority (2013). Report for 2011 on the results from the
monitoring of veterinary medicinal product residues and other substances in live
animals and animal products1. EFSA supporting publication 2013, EN-363. Available
at: www.efsa.europa.eu/publications. Assessed April 2014.
FAO/WHO (1995). Application of risk analysis to food standards issues. Report Joint
FAO/WHO Expert Consultation. WHO Document WHO/FNU/FOS 195.3. Geneva.
Available at:
http://www.who.int/foodsafety/publications/micro/en/march1995.pdf?ua=1.
Assessed April 2014.
http://ec.europa.eu/research/participants/data/ref/h2020/wp/2014_2015/main/h2020-wp1415-intro_en.pdf
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
147
Galarini R., Saluti G., Giusepponi D., Rossi R. & Moretti S. (2014). Multiclass determination
of 27 antibiotics in honey. Food Control. Available online April 2014.
Gosetti F., Mazzucco E., Zampieri D., & Gennaro M. (2010). Signal
suppression/enhancement in high-performance liquid chromatography tandem
mass spectrometry. Journal of Chromatography A, 1217, 3929-3937
Hammel Y.A., Mohamed R., Gremaud E., LeBreton M.H. & Guy P. (2008). Multi-screening
approach to monitor and quantify 42 antibiotic residues inhoneyby liquid
chromatography–tandem mass spectrometry. Journal of Chromatography A,
1177(1), 58-76.
Kaufmann A., Roth S., Ryser B. & Widmer M. (2002). Quantitative LC/MS-MS
determination of sulfonamides and some other antibiotics in honey. Journal of
AOAC International, 85 (4), 853-860.
Lopez M.I., Pettis J.S., Smith I.B. & Chu P. (2008). Multiclass Determination and
confirmation of antibiotics residues in honey using LC-MS/MS. Journal of
Agriculture and Food Chemistry, 56, 1553-1559.
Martinez Vidal J.L., Aguilera-Luiz M.M., Romero-Gónzalez R., & Garrido A.
(2009).Multiclass analysis of antibiotics residues in honey by ultraperformance
liquid chromatography-tandem mass spectrometry. Journal of Agriculture and
Food Chemistry, 57, 1760-1767.
Matuszewski B. K., Constanzer M. L. & Chavez-Eng C. M. (2003). Strategies for the
Assessment of Matrix Effect in Quantitative Bioanalytical Methods Based on HPLC-
MS/MS. Journal of Analytical Chemistry, 75, 3019-3030.
Maudens K., Zhang G-F. & Lambert W. (2004). Quantitative analysis of twelve
sulfonamides in honey after acidic hydrolysis by high-performance liquid
chromatography with post-column derivatization and fluorescence detection.
Journal of Chromatography A, 1047, 85-92.
Muñoz de la Peña A., Mora Diez N., Mahedero García M.C., Bohoyo Gil D. & Cañada-
Cañada F. (2007). A chemometric sensor for determining sulphaguanidine residues
in honey samples. Talanta 73, 304-313.
Sajid M., Na N., Safdarb M., Lu X., Ma L., Hec L. & Ouyanga J. (2013). Rapid trace level
determination of sulfonamide residues in honey with online extraction using short
C-18 column by high-performance liquid chromatography with fluorescence
detection. Journal of Chromatography A, 1314, 173-179.
Regulation (EC) No 470/2009 of the European Parliament and of the Council of 6 May
2009 laying down Community procedures for the establishment of residue limits of
pharmacologically active substances in foodstuffs of animal origin. Official Journal
L 152, 16 June 2009, pp. 11-22. Available at: http://eur-
lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:152:0011:0022:en:PDF
Routine quality control in honey packaging companies
as a key to guarantee consumer safety. The case of the presence of sulfonamides analyzed with LC-MS-MS
148
Rogatsky E., & Stein D. (2005). Evaluation of matrix effect and chromatography efficiency:
new parameters for validation of method development. Journal of the American
Society for Mass Spectrometry, 6, 1757-1759.
Sheridan R., Policastro B., Thomas S. & Rice D. (2008). Analysis and occurrence of 14
sulfonamide antibacterials and chloramphenicol in honey by solid-phase extraction
followed by LC/MS/MS analysis. Journal of Agriculture and Food Chemistry, 56,
3509-3516.
Staub-Spörri A., Jan P., Cognard E., Ortelli D. & Edder P. (Published online 07 Apr 2014).
Comprehensive screening of veterinary drugs in honey by ultra-high-performance
liquid chromatography coupled to mass spectrometry. Food Additives &
Contaminants.
Sterner J.L., Johnston M.V., Nicol G.R. & Ridge D.P. (2000). Signal suppression in
electrospray ionization Fourier transform mass spectrometry of multi-component
samples. Journal of Mass Spectrometry, 35, 385–391.
Taylor P. (2005). Matrix effects: The Achilles heel of quantitative high-performance liquid
chromatography–electrospray–tandem mass spectrometry. Clinical Biochemistry
38, 328-334.
Marisol Juan-Borrás, Eva Domenech, Isabel Escriche
Food Control (En Revisión)
3.6. Mixture-risk-assessment of pesticide residues in retail
polyfloral honey
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
151
Abstract
The presence of even tiny quantities of pesticide residues in honey, a traditional healthy
product, is a matter of concern for producers, packers and consumers. The aim of this
paper was to quantify the different pesticides in retail brands of polyfloral honey, and to
calculate the mixture risk assessment of honey for consumers (due to exposure to
pesticides), according to the results obtained from the analyzed samples. A LC-MS/MS
multi-residue method based on QuEChERS extraction was developed and validated for
13 compounds: 11 pesticides (chlorfenvinphos, coumaphos, tau-fluvalinate, amitraz, very
common in veterinary treatments and imidacloprid, acetamiprid, simazine,
cyproconazole, tebuconazole, chlorpiryphos-methyl, chlorpiryphos, widely used in
agricultural practices), and 2 metabolites of amitraz (2,4-DMA and 2,4-DMF). Results
showed that the samples contained pesticide residues at different concentration levels;
however, the MRL in honey for each of the 11 pesticides was not exceeded in any of the
cases. All the pesticides studied were found in at least one of the samples analysed; the
most common were amitraz (from 1 to 50 g/kg) present in 100% of the samples and
coumaphos (up to 14g/kg) in 63%. The hazard index (HI) for adults was less than 0.002
in all cases, a long way from 1, the value established as the limit of acceptability.
Therefore, commercial honey does not represent any significant risk to health. However,
taking into account that the residue levels should be present “as low as reasonably
achievable” it is deemed necessary to make an effort to reduce the presence of these
residues by appropriate agricultural and, above all, beekeeping practices, since acaridae
treatments are the main source of pesticides in honey.
Keywords
Honey; pesticides; Mixture-risk-assessment; MRL; hazard index
1. Introduction
Honey is a highly valued natural product due to its nutritional properties and appreciated
therapeutic applications. However, recently, food alerts caused by the detection of
antibiotics, pesticides or heavy metals in honey have jeopardized its healthy image (Juan-
Borrás et al., 2015). Pesticide residues in honey come from environmental pollution and
veterinary practices.
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
152
Honeybees come into contact with pesticides because veterinary practices expose them
to pesticides such as acaricides required to control bee parasites like Varroa destructor
and Varroa jacobsonie (Li et al., 2015)) and fungicides to control Ascosphera apis, as well
as other chemical agents such as antibiotics, and sulphonamides used to control bacterial
diseases like European foulbrood (Streptococcus pluton) or American foulbrood (Bacillus
larvae).
On the other hand, another source of contamination is a wide range of pathways such as
contaminated water, pollen and nectar from treated plants and crops, or even by direct
contact during flight (García-Chao et al., 2010; Rodriguez-Lopez et al., 2014). Currently in
Europe, the majority of insecticides used in agricultural practices are organophosphates,
carbamates and neonicotinoids, although in some countries the use of a number of them
is forbidden. These compounds affect the central nervous system of beneficial insects
such as honeybees, by inhibiting the activity of the enzyme acetylcholinesterase (Blasco
et al., 2011); Tanner & Czerwenka, 2011).
Combinations of sub-lethal doses of modern pesticides, of any origin, often produce
additive or even synergistic effects on the mortality and behaviour of animals, which
contributes to Colony Collapse Disorder (CCD) (Laetz et al., 2009; Van Engelsdorp et al.,
2009).These pesticides not only affect the insects, but also contaminate bee products like
honey. The determination of contaminants and residues in honey and other bee products
has become a growing concern in recent years, especially as these compounds may
diminish the beneficial properties of honey and, if present in significant amounts, may
pose a serious threat to human health (Kujawski & Namiesnik, 2011).
In order to protect human health, chemical hazards must be controlled to stop pesticides
reaching the food chain (Blasco et al., 2011; Barganska et al., 2013). With this aim in mind,
Regulation (EC) Nº 396/2005 and Regulation (EC) Nº 37/2010 established MRLs for
residues of certain specific pharmacologically active substances in foodstuffs of animal
origin (for instance, coumaphos and amitraz in honey). This lowering of the limits of
detection in a matrix as complex as honey is only possible thanks to modern analytical
techniques (GC-MS-MS and LC-MS/MS) and adequate extraction and cleaning of analytes
using QuEChERs (Blasco et al., 2011; Barganska et al., 2013; Hou et al., 2013).
Currently, chemicals are routinely assessed on a chemical-by-chemical basis, however,
consumers could be exposed to multiple chemicals via different food types, and
consequently, this approach may not be sufficiently protective. Hence, a new metric
known as Mixture Risk Assessment (MRA) has been introduced to assess the cumulative
risk to human health (Zheng et al., 2007; Boobis et al., 2008; Kortenkamp et al., 2009);
Evans et al., 2015; Yu et al., 2016). The existence of a mixture is not always an indication
of a risk to human or environmental health, but indicates the necessity of examining
whether more accurate estimations of risk will be produced by considering all of the
chemicals that are present.
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
153
Therefore, the objective of this paper was to quantify the presence of different pesticides
in polyfloral labeled brands, and to estimate the mixture risk assessment of the honey for
consumers (due to exposure to pesticides) based on the results obtained from the
analyzed samples. As a first step, the analytical procedure was validated in order to
guarantee the quality of the results obtained.
2. Materials and Methods
2.1.-Honey samples
This study was carried out with supermarket own-brands and well-known brands, which
represent almost all of the retail sales in the Spanish market. A total of 22 honey samples,
labelled as polyfloral were purchased across Spain from different retail outlets.
A mixture of 5 polyfloral honeys without the compounds analyzed in this study was
selected as a “blank honey” in order to perform the validation procedure of the
methodology.These samples were provided directly by Spanish beekeepers.
In all the samples the percentage of pollen of the different botanical species was
evaluated in order to estimate their geographical origin. There was only one sample for
which it was not possible to perform this characterization due to the insufficient presence
of pollen.
2.2.-Analytical determinations
2.2.1. Pesticide analysis
Standards and reagents
Table 1 shows the 11 pesticides analysed in the present work. All of them [including the
two metabolites of amitraz: 2,4-DMA (2,4-dimethylaniline) and 2,4-DMF (N-2,4-
dimethylphenyl formamide)], were purchased from Sigma-Aldrich, (Steinheim,
Germany), with a purity of ≥99%.Individual stock standard solutions (200 μg/mL) were
prepared in methanol (MeOH) or acetonitrile (MeCN), depending on the solubility of each
pesticide, and stored at -20º C. These solutions are stable for at least 1 year. From them,
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
154
a stock standard mixture of 13 pesticides was made in MeCN (each analyte at a
concentration of 40μg/mL) and stored in amber glass vials at -20º C.
The spiking solutions (10μg/mL and 1μg/mL) were prepared in ultrapure water from the
stock standard mixture solution and stored in amber glass vials at -4º C.
The MeOH used for the mobile phase was MS-Grade (Scharlab, Barcelona). The MeOH
and MeCN used for sample extraction (Quechers methodology) and for the standards
preparation was HPLC grade and was obtained from Prolabo (VWR, France).
Table 1. Pesticides studied in the present work.
Common
name
Biocide
action
Pesticide
family
MRLa ADI b WHOc
Veterinary practices
Amitraz Acaricide Formamidine 200(1) 10 III
Chlorfenvinphos Acaricide Organophosphorus 10 0.5 IB
Coumaphos Acaricide Organophosphorus 100 0.25 IB
Tau-fluvalinate Acaricide Pyrethroid 50 5 U
Agricultural practices
Acetamiprid Insecticide Neonicotinoid 50 70 II
Chlorpyrifos Insecticide Organophosphorus 10 10 II
Chlorpyriphos-methyl Insecticide Organophosphorus 10 10 III
Cyproconazole Fungicide Azole 50 10 III
Imidacloprid Insecticide Neonicotinoid 50 60 II
Simazine Herbicide Triazine 10 5 U
Tebuconazole Fungicide Azole 50 30 III aMRL (Maximun Residues Level); mg kg-1 (EU Pesticides database, 2015; Regulation EU 37/2010) bADI (Acceptable Daily Intake); mg kg-1 d-1 (WHO, 2012) c (WHO, 2010); IB = Highly hazardous; II = Moderately hazardous; III = slightly hazardous; U =
Unlikely to present acute hazard in normal use (1)The metabolites of amitraz (2,4-DMA and 2,4-DMF) are included
The formic acid (purity 99%) for LC-MS analysis, analytical grade sodium chloride (NaCl),
anhydrous magnesium sulphate (MgSO4), disodium hydrogen citrate sesquihydrate (di-
Na), trisodium citrate dihydrate (tri-Na) and Bondesil Primary-Secondary Amine (PSA)
were provided by Sigma Aldrich (Saint Quentin Fallavier, France). Ultrapure water was
obtained from a Milli-Q® water purification system connected to a LC-PAK cartridge to
remove the remaining organic contaminants at trace levels (Millipore, Molsheim, France).
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
155
Extraction procedure
Extracts were prepared using QuEChERS as it is one of the most commonly applied
procedures for pesticide residue extraction. 5g of sample were weighed into a
polypropylene tube. After addition of 10 mL of MeCN, the sample was extracted by
shaking by hand for at least 1 min, and by vortex 1 more minute. Then a salt mixture
(NaCl, MgSO4, di-Na, tri-Na) was added and the tube was shaken vigorously for a few
seconds to prevent agglomeration. After shaking for 1 min the tubes were centrifuged at
1500g to obtain a clarified MeCN extract. The supernatant was transferred into another
tube (PSA) shaken for 1 min and centrifuged at 1500g for 4 min. A 1 mL aliquot was taken
and filtered through a nylon 0.45 m membrane filter. This final extract was injected in
the LC-MS/MS system.
LC-MS/MS analysis
The analyses were performed using an Agilent 1200 Series Rapid Resolution Liquid
Chromatograph (Agilent, Palo Alto, CA) equipped with a Cortecs column (100 mm × 2.1
mm I.D., 2.7 μm particle size, Waters; Milford, MA, USA) maintained at 30 ºC. The mobile
phase consisted of MeOH and water acidified with 0.1% of formic acid. The gradient was
applied at a flow rate of 0.4 mL/min as follows: initial conditions of 5% MeCN increased
linearly to 20% for 30s, then increased linearly to 100% at min 6, held at 100% for 2 min
and returned to initial conditions for 5 min. The inject volume was 5 L.
The MS–MS detection was performed using an Agilent 6410 Series Triple Quadruple
(Agilent, Palo Alto, CA). The mass spectrometer was operated using electrospray
ionization in the positive ion mode (ESI+). The capillary voltage was set to 4.0 kV. The
source temperature was 350 ºC and the desolvation temperature was 350 °C. Nitrogen
was used as the desolvation gas (flow 12 L/min) and collision gas at a pressure of 40 psi.
Detection was performed in the multiple reaction monitoring (MRM) mode, this function
automatically optimized the dwell times according to the number of simultaneously
detected MRM transitions. The transition with the highest intensity was used as a
quantifier and the second transition as a qualifier. Fragment voltage and collision energy
were optimized for each pesticide, masses of precursor ions and product ions are
summarized in Table 2.
2.2.2. Validation of the analytical method
The pesticide analytical methodology applied in this work was validated for every
compound to ensure the reliability of the results in the quantification range considered.
To this end, following the SANCO 12571/2013 guidance, the parameters: linearity,
recovery, precision (repeatability or intraday precision “RSDr” and reproducibility or
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
156
interday precision “RSDR”), limit of detection (LOD) and limit of quantification (LOQ) were
calculated.
Table 2. Settings for the ion transitions of the thirteen selected pesticides studied in MRM mode
Compound
Precursor Ion
Product Ion 1 (m/z)
Product Ion 2 (m/z)
Collision energy
(V)
Retention Time (min)
Amitraz 294.2 163.0 122.0 10;35 10.20
2,4-DMA 122.1 77.0 107.0 30 6.20
2,4-DMF 150.0 107.0 123.2 18;13 8.40
Chlorfenvinphos 359.0 154.7 126.8 10;15 9.49
Coumaphos 363.0 306.6 226.4 10;40 9.52
Tau-fluvalinate 503.1 180.9 207.7 37;5 10.30
Acetamiprid 223.0 126.0 56.0 15 7.72
Chlorpiryphos 351.6 200.0 97.0 15 9.99
Chlorpiryphos-methyl 324.0 125.0 292.0 15 9.66
Cyproconazole 292.1 69.9 124.7 17;37 9.28
Imidacloprid 256.1 209.3 175.4 15;20 7.30
Simazine 202.1 132.0 123.9 20 8.63
Tebuconazole 308.2 69.9 124.7 21;5 9.48
2.2.3. Risk Evaluation
Estimated daily intake (EDI)
This parameter, expressed as g kg-1 d-1, is obtained as follows Eq 1:
𝐸𝐷𝐼 =C∗Con
Bw 1
Where C (µg kg-1) is the average concentration of a given pesticide in the collected
honeys; Con (kg person-1 d-1) is the daily average consumption of honey in Spain; Bw (kg
person-1) represents body weight. Based on the report by the Instituto Nacional de
Estadística (Spanish National Institute of Statistics), the average honey consumption for
adults is 0.7 kg per person per year, and the average body weight is 70 kg for adults (INE,
2012).
Hazard quotient (HQ)
This parameter is calculated for each pesticide by dividing the estimated daily intake (EDI)
by the acceptable daily intake (ADI) (g kg-1 d-1) for each pesticide (Table 1), (USEPA,
2007; Evans et al., 2015), Eq 2.
𝐻𝑄 =EDI
ADI 2
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
157
Evaluation of hazard index (HI)
The HI is a measurement of the potential risk of adverse health effects from a mixture of
chemical constituents (Zheng et al., 2007; Evans et al., 2015).The Hazard Index (HI) is used
in most MRA (Mixture risk Assessment) approaches. The HI due to daily average
consumption of honey for a human being is obtained as the sum of the hazard quotient
(HQ) calculated for each chemical, Eq 3:
𝐻𝐼 = ∑ 𝐻𝑄𝑛𝑖𝑛=1 3
Conventionally, a HI less than 1 indicates that the total exposure does not exceed the
level considered to be “acceptable”, and people are unlikely to be exposed at a toxic level
with possible consequences for health. On the contrary, if it exceeds one, there is a
possibility of suffering adverse effects, (Evans et al., 2015; Yu et al., 2016).
3. Results and Discussion
3.1.-Matrix effect and in-house validation method
Since co-extracted matrix constituents may cause ion suppressions or ion enhancement,
therefore interfering with the quantitative result, the first step in the validation process
was the evaluation of the matrix effect. To this end, for every studied compound, matrix
effects were tested by comparing the slopes of the calibration curves obtained for the
standard solutions prepared in solvent (MeCN) with those prepared in blank matrix
extracts (mix of 5 honeys). In general, medium or low matrix effects were observed, and
therefore the calibration curves were constructed using the blank matrix extracts to avoid
these effects. The levels of concentrations considered were: 3, 10, 20, 50, 100 and, 200
g/kg. In addition, the level of 400 μg/kg was included in the calibration curve for amitraz;
this is because this value corresponds to twice the MRL of this compound, as specified by
SANCO guidelines.
Table 3 shows the data for the validation parameters. Linearity (expressed as R2), was in
all cases higher than 0.9977 throughout the concentration range considered.
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
158
The recoveries of most of the studied compounds were in a range between 70 and 120%,
complying with the requirements of SANCO 12571/2013. On very few occasions the
recoveries were less than 70% (60% and 66% for coumaphos at levels 3 g/kg and 10
g/kg, respectively, and 66% for 2,4-DMA at the level 50 g/kg). Amitraz was the most
problematic compound because the recoveries ranged between 60 and 76% for 4 of the
concentration levels used.
With respect to repeatability (RSDr) and reproducibility (RSDR), all the pesticides studied
were in agreement with SANCO 12571/2013, since the values were less than 20%. The
only exception was coumaphos with 22.2% at the level of 10 µg/kg for repeatability, and
coumaphos and amitraz, at the level of 20 µg/kg, with 21.0% and 24.5% in the case of
reproducibility.
In the present study 3 g/kgwas considered to be the LOQ (limit of quantification)
because it was the lowest validated point of the calibration curve for all the compounds,
with acceptable trueness and precision results (SANCO 12571/2013). As ten times LOD is
equal to three times LOQ, the LOD of the method was set at 1g/kg, in all cases.
Being a multi-residue analysis, in general, the values obtained for all the validation
parameters can be considered acceptable. It is usual to reach a compromise in order to
analyze all the compounds considered together (Kujawski & Namiesnik, 2011).
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
Table 3. Validation parameters for the chemical compounds studied.
ll
Analyte 3 μg/kg 10 μg/kg 20 μg/kg 50 μg/kg 100 μg/kg 200 μg/kg 400 μg/kg
R2 Recovery RSDr Recovery RSDr Recovery RSDr RSDR Recovery RSDr Recovery RSDr Recovery RSDr Recovery RSDr
Amitraz 0.9988 60 (10) 12.8 66 (6) 16.8 67 (13) 12.6 24.5 76 (5) 15.4 70 (8) 10.7 63 (5) 10.0 69 (8) 8.4
2,4-DMA 0.9990 76 (3) 5.0 70 (4) 5.1 74 (3) 4.1 7.2 66 (4) 5.4 70 (2) 2.2 79 (1) 0.8
2,4-DMF 0.9996 98 (3) 3.5 101 (7) 4.8 92 (4) 4.8 0.9 86 (2) 2.4 86 (2) 2.5 91 (4) 4.9
Chlorfenvinphos 0.9989 93 (4) 9.0 96 (13) 13.9 97 (6) 5.8 7.6 89 (6) 7.3 98 (4) 4.2 102 (2) 1.7
Coumaphos 0.9993 60 (14) 20.0 66 (34) 22.2 90 (10) 11.2 21.0 94 (10) 10.8 95 (8) 8.0 101 (2) 1.8
Tau-fluvalinate 0.9977 80 (5) 8.2 87 (6) 7.4 84 (3) 4.0 8.9 82 (2) 2.8 81 (2) 2.0 87 (3) 3.4
Acetamiprid 0.9997 98 (6) 4.6 101 (7) 7.2 97 (2) 2.6 3.8 91 (3) 3.1 95 (2) 2.1 99 (1) 0.8
Chlorpiryphos 0.9990 85 (2) 10.2 89 (17) 18.5 86 (7) 6.5 17.1 78 (6) 8.1 86 (3) 3.4 92 (3) 2.9
Chlorpiryphos-methyl 0.9988 82 (12) 5.0 80 (14) 5.2 87 (2) 2.5 16.8 79 (4) 5.3 87 (2) 2.7 91 (2) 2.0
Cyproconazole 0.9998 90 (4) 6.6 91 (7) 7.4 93 (5) 4.9 5.1 85 (4) 4.5 90 (2) 2.1 95 (1) 0.9
Imidacloprid 0.9998 102 (4) 5.2 113 (9) 8.1 102 (3) 2.8 5.0 98 (3) 3.6 96 (2) 1.9 98 (1) 0.7
Simazine 0.9997 95 (2) 6.0 101 (7) 7.0 92 (3) 3.0 5.6 89 (3) 3.1 94 (1) 1.1 96 (1) 0.9
Tebuconazole, 0.9996 101 (8) 9.0 104 (2) 11.7 100 (4) 4.4 4.3 96 (5) 4.7 97 (3) 2.7 102 (2) 1.7
15
9
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
160
3.2.-Analysis of the honey samples
Few studies regarding the monitoring of pesticide residue levels in honey produced in
Spain have been published previously (Blasco et al., 2003; Blasco et al., 2011); what is
more, own-brands which contain mixtures from EU and non-EU countries honey have not
been studied at all. Blasco et al. 2003 analyzed 42 pesticide residues (organochlorine,
carbamate, and organophosphorus) in 50 samples of honey collected from local markets
in Spain and Portugal during 2002; they found that of the 26 honey samples from Spain
16 (61%) samples were contaminated with at least one pesticide, and of the 24
Portuguese samples, pesticide residues were detected in 23 (95%) samples.
Table 4 shows the concentration levels of pesticide residues obtained in the present
work. All the pesticides studied were found in at least one of the samples investigated. In
all cases the MRL established by the EU was satisfied. The pesticide which was present in
all of the samples was amitraz (100%), the next most frequently occurring was
coumaphos (63.6%), followed by pesticides such as acetamiprid (45.4%) and simazine
(40.9%) used in agricultural practices.
Amitraz and coumaphos are widely used in veterinary practices, in several European
countries and the United States, due to their effectiveness against the varroa acarus. This
results in the habitual presence of these compounds in honey (Lambert et al., 2013). In
the present work amitraz was found at an average value of 12.4 µg/kg (n=22), followed
by coumaphos 4 µg/kg (n=14). Similar results were reported by Gómez-Pérez et al.in
2012, who detected 5.1 µg/kg of coumaphos in one Spanish sample. Barganska et al.
(2013) quantified coumaphos in 6 out of 45 (13%) Polish samples, where the
concentration ranged from less than the limit of quantification (4.95 µg/kg) to 16.7 µg/kg.
A higher percentage of coumaphos in honey (n=186) was found by the USDA 32.3%
(USDA, 2015).
Lambert et al., 2013 studied the presence of 80 pesticides in honey, pollen and honey
bees, obtained directly from apiaries in France. These authors found that the frequency
of detection of these compounds was higher in the honey samples (28/141) than in pollen
(23/128) or honey bee (20/141) samples. These authors observed that the acaricides,
coumaphos (78.0%) and amitraz (68.8%) were the most frequently detected residues in
honey.
In relation to chlorfenvinphos and tau-fluvalinate, in the present work the results showed
that the first one was detected in 36.4% (n=8) of the samples with a mean concentration
of 1.8 µg/kg and tau-fluvalinate 13.6% (n= 3) with an average value of 1.3 µg/kg. Similar
values were found (12.3%) for tau-fluvalinate by the USDA pesticide data program (USDA,
2015).
Acetamiprid and simazine, which are related to agricultural practices, were found in the
present study at mean values of 2.9 and 2.6 µg/kg, respectively. Similar values were found
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
161
by other authors who detected compounds that come from crop treatment which were
also below the limits (Rissato et al., 2007; Barganska et al., 2013). In the present work
chlorpyriphos was present in 10 samples (18.2%) and imidacloprid in 9 (13.6%) samples;
with mean concentrations of 1.3 µg/kg and 2 µg/kg, respectively. Comparing these results
with published data, Rodriguez-Lopez et al. in 2014, detected chlorpyriphos in 50.1% (31
out of 61 samples), however only two samples were higher than the limit of quantification
(LOQ=5 µg/kg), with mean values of 6 and 21 µg/kg, respectively. Panseri et al., 2014
detected chlorpyriphos in 33% of the samples with a mean concentration of 5.6 µg/kg.
The results obtained by Rissato et al. in 2007, indicated a low level of contamination by
pesticide residues, nevertheless, honey sampled in 2003 and 2004 had a concentration
of 10 and 15 µg/kg of chlorpyrifos, respectively, while tau-fluvalinate was not detected.
In summary, 100% of honey samples contained at least one of the pesticides studied.
Particularly, around 40% of the honey samples presented less than three pesticides and
18% only one pesticide. However, it is highlighted that in any case, the MRL was
exceeded.
3.3.-Mix risk assessment
Nowadays, there is concern that the chemical by chemical approach may not be
sufficiently protective, especially when it is well known that humans are exposed to more
than one chemical at a time (Evans et al., 2015). The measure of the potential risk of
adverse health to consumers due to the presence of a mixture of pesticides in honey is
evaluated in the present work through the estimation of the hazard index (HI). This is
shown in Figure 1 for the mixture of pesticides in the 22 honey samples (own-brands and
well-known brands labelled as polyfloral), calculated as the sum of the hazard quotient
(HQ) for each pesticide. The different colours in the bars refer to the contribution of each
pesticide (HQ) to the HI value of each brand. On the x axis, next to the code for each
sample (from B1 to B22) appears the information about the countries of origin. It is well
known that honey companies mix raw material from different sources to make up a
production batch.
The HI values ranged between 5.5 10-6 in B9, and 1.8 10-3in B8. This means that in the
worst case, the HI value was 500 times lower than 1, the limit of acceptability. Few studies
have been reported about the HI values for pesticides in other types of food. Among
them, the study carried out by Yu et al. in 2016 should be highlighted. These authors
conclude that the HI for adults for fresh vegetables (n= 214) due to 11 pesticides in
Changchun (China) was 0.44. This value is less than half of the limit of acceptability;
however it is much higher than that estimated for honey here. This is mainly due to the
large quantitative difference in the consumption of both types of food. Therefore citizens
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
162
are not exposed to a toxic level of pesticides via honey in sufficiently large quantities to
cause possible health consequences (Evans et al., 2015; Yu et al., 2016).
Table 4. Average concentration (µg/kg) of pesticides present in the 22 own-brand and well-known
brand samples of honey (n=3).
Honey branch
Am
itra
z*
Ch
lorf
envi
np
ho
s
Co
um
aph
os
Tau
-flu
valin
ate
Ace
tam
ipri
d
Ch
lorp
yrip
ho
s
Ch
lorp
yrip
ho
s-
met
hyl
Cyp
roco
naz
ole
Imid
aclo
pri
d
Sim
azin
e
Teb
uco
naz
ole
B1 8 2 8 1 n.d. n.d. n.d. n.d. n.d. n.d. n.d.
B2 21 n.d. 2 n.d. 3 n.d. n.d. n.d. n.d 2 n.d.
B3 5 1 5 1 3 n.d. n.d. 5 3 3 4
B4 3 n.d. 1 n.d. 6 n.d. n.d. n.d. n.d. n.d. n.d.
B5 7 1 7 n.d. 2 n.d. n.d. n.d. n.d. n.d. n.d.
B6 43 2 2 n.d. n.d. 1 n.d. n.d. n.d. 1 n.d.
B7 12 n.d. 1 n.d. n.d. n.d. n.d. n.d. n.d. 1 n.d.
B8 50 4 13 2 2 2 2 2 2 3 2
B9 2 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.
B10 2 n.d. n.d. n.d. 4 n.d. n.d. n.d. n.d. n.d. n.d.
B11 5 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.
B12 13 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 2 n.d.
B13 3 1 n.d. n.d. 2 n.d. n.d. n.d. n.d. 4 n.d.
B14 4 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.
B15 5 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.
B16 8 n.d. n.d. n.d. 3 1 n.d. n.d. n.d. 2 n.d.
B17 3 n.d. 6 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.
B18 6 n.d. 2 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.
B19 7 1 2 n.d. 1 n.d. n.d. n.d. 1 n.d. n.d.
B20 4 n.d. 1 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.
B21 30 n.d. 3 n.d. 3 n.d. n.d. n.d. n.d. n.d. n.d.
B22 31 2 4 n.d. n.d. 1 n.d. n.d. n.d. 5 n.d.
N 22
(100%)
8
(36.4%)
14
(63.6%)
3
(13.6%)
10
(45.4%)
4
(18.1%)
1
(4.5%)
2
(9.1%)
3
(13.6%)
9
(40.9%)
2
(9.1%)
n.d.= non detected
N= Number of samples with presence of pesticides
*= amitraz and its metabolites
In general, coumaphos followed by chlorfenvinphos had the highest contribution to the
HI values, 71% and 15%, respectively. It should be noted that both pesticides are classified
as “highly hazardous” by the WHO and “highly toxic” by the WHO (2010). Third was
amitraz HQ (8%), which despite being classified as “moderately hazardous” by OMS, in
the present paper had the highest exposure (100% of the honey brands sampled).
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
163
Simazine represented 2%, followed by chorpyrifos-methyl, chlorpyrifos, cyproconazole
and tau-fluvalinate, all of them with 1%. Therefore, 94% are due to veterinary practices.
Figure 1. Hazard Index of the mixture of 11 pesticides in the 22 samples (own-brands and well-known brands labelled as polyfloral). Different colours in the bars refer to the contribution of each pesticide (HQ) in the HI of each brand. Country origin: SE (Southern Europe); Ch (China); CA (Central America); SA (South America); Th (Thailand); Ce (Chile); ? (unknown origin).
Considering the country of origin of honey, it is observed that the presence of pesticide
residues does not conform to a geographic pattern. In this regard, it is noted that a
country can be associated with both high HI values and very low HI values. Therefore, the
presence of pesticides may be associated with specific beekeeping practices carried out
against the varroa acarus. This can vary from year to year because the propagation of this
parasite is greatly influenced by weather conditions (Garrido-Bailón et al., 2012).
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
164
4. Conclusion
The analytical procedure developed to determine eleven pesticide residues in honey
permits a level of quantification of 3 g/kg and detection of 1g/kg. All the pesticides
studied were found in at least one of the retail polyfloral brands analysed. The highest
percentages correspond to those that come from veterinary treatments, especially
amitraz and coumaphos, followed by pesticides such as acetamiprid and simazine used
in agricultural practices. The samples contained pesticide residues at different
concentration levels, however, the MRL in honey for each of the 11 pesticides was not
exceeded, in any of the cases.
In relation to the individual hazard quotient, coumaphos followed by chlorfenvinphos and
amitraz had the highest contribution to the HI values, which means that, veterinary
treatments are the main source of pesticides in honey. However, the hazard index (HI)
for adults was always less than 0.002, infinitesimal compared to the value of 1 recognized
as the level considered to be “acceptable”. Therefore, it is concluded that the daily intake
of pesticides through brands labelled as polyfloral honey is not an important pathway for
the dietary exposure of citizens and consequently does not entail a significant health risk.
However, we should not be satisfied with this, but strive to attain the ALARA principle (As
Low As Reasonably Achievable), by which residues have to be eliminated or minimized as
much as possible. This requires an effort by the primary sector: farmers and beekeepers,
as their practices have the greatest influence on this problem.
Acknowledgment
This study forms part of a project funded by the Ministerio de Educación y Ciencia of
Spain (Programa Estatal de Investigación Desarrollo e Innovación Orientada a los retos de
la sociedad; Project number AGL2013-48646-R), for which the authors are grateful.
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
165
5. References
Barganska Z., Slebioda M. & Namiesnik J. (2013).Pesticide residues levels in honey from
apiaries located of Northern Poland. Food Control, 31, 196-201.
Blasco C., Fernandez M., Pena A., Lino C., Silveira I., Font G. & Pico Y. (2003). Assessment
of Pesticide Residues in Honey Samples from Portugal and Spain. Journal of
Agricultural and Food Chemistry, 51, 8132-8138.
Blasco C., Vázquez-Roig P., Onghena M., Masia A. & Picó Y. (2011). Analysis of insecticides
in honey by liquid chromatography-ion trap-mass spectrometry: Comparison of
different extraction procedures. Journal of Chromatography A, 1218, 4892-4901.
Boobis A. R., Ossendorp B. C., Banasiak U., Hamey P. Y., Sebestyen I., & Moretto A. (2008).
Cumulative risk assessment of pesticide residues in food. Toxicology letters 180 (2),
137-150.
EU Pesticides database European Commission. Available at:
http://ec.europa.eu/food/plant/pesticides/eu-pesticides-database.
Evans R. M., Scholze M. & Kortenkamp A. (2015). Examining the feasibility of mixture risk
assessment: A case study using a tiered approach with data of 67 pesticides from
the Joint FAO/WHO Meeting on Pesticide Residues (JMPR). Food and Chemical
Toxicology, 84, 260-269.
García-Chao M., Agruña M. J., Flores Calvete G., Sakkas V., Llompart M. & Dagnac T.
(2010). Validation of an off line solid phase extraction liquid chromatography-
tandem mass spectrometry method for the determination of systemic insecticide
residues in honey and pollen samples collected in apiaries from NW Spain. Analytica
Chimica Acta. 672, 107-113.
Garrido-Bailón E., Botías C., Martin-Hernandez R., Meartinez-Salvador A., Meana A. &
Higes M. (2012). Prevalencia de los principales Agentes Patógenos de Apis mellifera
iberiensis en la cabaña apícola Española. Anales Real Academia de Ciencias
Veterinarias de Andalucía Oriental. 25 (1), 109-130.
Gómez-Pérez M. L., Plaza-Bolaños P., Romero-González R. Martínez-Vidal, J.L. & Garrido-
Frenich A. (2012). Comprehensive qualitative and quantitative determination of
pesticides and veterinary drugs in honey using liquid chromatography-Orbitrap high
resolution mass spectrometry. Journal of ChromatographyA, 1248, 130-138
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
166
Hou X., Han M., Dai X., Yang X. & Yi S. (2013). A multi-residue method for the
determination of 124 pesticides in rice by modified QuEChERS extraction and gas
chromatography–tandem mass spectrometry. Food Chemistry, 138, 1198–1205.
Juan-Borrás M., Periche A., Domenech E. & Escriche I. (2015). Routine quality control in
honey packaging companies as a key to guarantee consumer safety. The case of the
presence of sulfonamides analyzed with LC-MS-MS. Food Control, 50, 243-249.
Kortenkamp A., Backhaus T. & Faust M. (2009). State of the Art on Mixture
Toxicity.Report. Available at:
http://ec.europa.eu/environment/chemicals/pdf/report_Mixture%20toxicity.pdf.
Kujawski M. W. & Namiesnik J. (2011). Levels of 13 multi-class pesticide residues in Polish
honeys determined by LC-ESI-MS/MS. Food Control. 22, 914-919.
Lambert O, Piroux M, Puyo S, Thorin C, L’Hostis M, Wiest L., Bulete A., Delbac F. &
Pouliquen H. (2013) Widespread Occurrence of Chemical Residues in Beehive
Matrices from Apiaries Located in Different Landscapes of Western France.
Available at:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0067007
Li Y., Kelley R. A., Anderson T. D. & Lydy M. J. (2015). Development and comparison of
two multi-residue methods for the analysis of select pesticides in honey bees,
pollen, and wax by gas chromatography-quadrupole mass spectrometry. Talanta.
140; 81-87.
Panseri S., Catalano A., Giorgi A., Arioli F., Procopio A., Britti D. & Chiesa L.M. (2014).
Occurrence of pesticide residues in Italian honey from different areas in relation to
its potential contamination sources. Food Control, 38, 150-156.
Regulation (EU) Nº 37/2010 of 22 December 2009 on pharmacologically active
substances and their classification regarding maximum residue limits in foodstuffs
of animal origin. Available at:http://ec.europa.eu/health/files/eudralex/vol-
5/reg_2010_37/reg_2010_37_en.pdf.
Regulation (EC) Nº 396/2005 of the European Parliament and of the Council of 23
February 2005 on maximum residue levels of pesticides in or on food and feed of
plant and animal origin and amending Council Directive 91/414/EEC, European
Community, Brussels, 16-3-2005.Available at: http://eur-
lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2005:070:0001:0016:en:PDF.
Rissato S. R., Galhiane M. S., de Almeida M. V., Gerenutti M. & Apon B. M. (2007).
Multiresidue determination of pesticides in honey samples by gas
chromatography–mass spectrometry and application in environmental
contamination. Food Chemistry, 101(4), 1719-1726.
Mixture-risk-assessment of pesticide residues in retail polyfloral honey
167
Rodriguez-López D., Ahumada D. A., Díaz A. C. & Guerrero J. A. (2014). Evaluation of
pesticide residues in honey from different geographic regions of Colombia. Food
Control, 37, 33-40.
SANCO 12571/2013. (2013). Method Validation and Quality Control Procedures for
Pesticide Residue Analysis in Food and Feed. Available at:
http://ec.europa.eu/food/plant/pesticides/guidance_documents/docs/qualcontro
l_en.pdf.
Tanner G. & Czerwenka C. (2011). LC-MS/MS analysis of neonicotinoid insecticides in
honey: methodology and residue findings in Austrian honeys. Journal of Agricultural
and Food Chemistry, 59 (23), 12271-12277.
USEPA U.S. (Enviromental Protection Agency). (2007). Integrated risk information
system-database (Philadelphia PA; Washington, DC).
USDA 2015. Pesticides data program. Available at:
http://www.ams.usda.gov/datasets/pdp.
Van Engelsdorp D., Evans J., Saegerman C., Mullin C., Haubruge E, Nguyen B.K., Frazier
M., Frazier J., Cox-Foster D., Chen Y., Underwood R., Tarpy D.R.& Pettis J.S. (2009).
Colony Collapse Disorder: A Descriptive Study. Available at:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0006481.
WHO (World Health Organization) (2010). The WHO recommended classification of
pesticides by hazard. Available at:
http://www.who.int/ipcs/publications/pesticides_hazard/en/
WHO (World Health Organization) (2012). Inventory of evaluations performed by the
Joint Meeting on Pesticide Residues (JMPR). Available at:
http://apps.who.int/pesticide-residues-jmpr-database/Home/Search
Yu R., Liu Q., Liu J., Wang Q. & Wang Y. (2016). Concentrations of organophosphorus
pesticides in fresh vegetables and related human health risk assessment in
Changchun, Northeast China. Food Control, 60, 353-360.
Zheng N., Wang Q., Zhang X., Zheng D., Zhang Z. & Zhang S. (2007). Population health risk
due to dietary intake of heavy metals in the industrial area of Huludao city, China.
Science of the Total Environment, 387(1), 96-104.
4. CONCLUSIONES
Conclusiones
171
4. Conclusiones
4.1.-Conclusiones del objetivo general 1
Aportaciones:
Journal of Chemistry (2015), http://dx.doi.org/10.1155/2015/929658.
International Journal of Food Science and Technology (2015), 50(7), 1690-1696.
Food Research International (2014), 60, 86-94.
Food Control (En revisión).
4.1.1. El control de determinados parámetros físico-químicos en la etapa de recepción de
la industria de envasado de miel, es fundamental para garantizar la calidad del producto
final y el cumplimiento de los requisitos legales. Llegar a conocer el origen de la
variabilidad de estos parámetros es esencial para tomar medidas en la mejora de la
calidad de la miel. En este sentido, en la etapa de recepción se evaluó la influencia de la
variedad de miel, año de cosecha, así como el papel del apicultor sobre la variabilidad del
HMF, la humedad y el color. Se analizaron 1593 muestras de 11 variedades de miel
(botánicamente clasificadas), de 98 apicultores, cosechadas entre 2009 y 2013. Las
mieles más claras presentaron mayor humedad, mientras que las mieles más oscuras
mostraron valores de humedad más bajos. El color fue el parámetro más afectado tanto
por la variedad de la miel como por el año de la cosecha, mientras que el HMF fue el
menos influenciado por ambos atributos. Para el HMF y la humedad, se observaron
muestras que presentaban algunos valores extremos, legalmente inaceptables,
provenientes en todos los casos de ciertos apicultores. Esto demuestra el importante
papel que el apicultor tiene sobre ciertos parámetros que afectan a la calidad de la miel,
e incluso sobre el color varietal característico que el mercado requiere. Por lo tanto, unas
buenas prácticas apícolas son vitales para obtener el producto que el consumidor espera
y la legislación exige.
4.1.2. Se ha evaluado el nivel de antranilato de metilo (MA) que comúnmente tienen las
mieles de cítricos españolas y la posible correlación con el porcentaje de polen de Citrus
spp. El análisis melisopalinológico por sí solo no es suficiente para la clasificación de
algunas variedades de miel, como es el caso de la miel de cítrico, ya que el porcentaje de
polen en este tipo de miel puede ser menor de lo esperado (variedades híbridas estériles
Conclusiones
172
o producción de polen y néctar no simultánea). Por ello, se analizaron muestras de mieles
vendidas como cítrico y procedentes de apicultor, ambas de las campañas de 2011 y
2012. El polen osciló entre 1 y 88% y el MA entre 0.5 y 5.9 mg/kg, no habiendo correlación
cuantitativa entre ambos. Sin embargo, se observaron correlaciones significativas con
coeficientes de Pearson moderados entre algunos de los parámetros fisicoquímicos
evaluados en las muestras [MA/conductividad eléctrica (-0.678); MA/de color (-0.559);
polen /conductividad eléctrica (-0.553) y polen color (-0.556)]. Un análisis de tabla de
contingencia (tabulaciones cruzadas) llevado a cabo para evaluar la interrelación entre el
polen y MA (teniendo en cuenta estas variables como categóricas: al menos un 10% de
polen de Citrus spp.; mínimo contenido de MA: 2 mg/kg) mostró que el 53.5% de las
muestras en 2011 y el 56.8% en 2012 cumplieron tanto con el porcentaje de polen como
con la concentración de MA requeridos. Por otro lado, el 35.7% en 2011 y el 38.6% de las
muestras en 2012, aun cumpliendo con el porcentaje de polen no cumplían con el MA.
Además el 4.6% de las muestras que cumplían con MA, no lo hacían con el polen. Este
hecho también ha sido observado por otros autores en mieles de cítrico italianas. En este
trabajo se propone reconsiderar el nivel de MA requerido para la miel de cítricos
españoles, aplicando un valor más realista. Pero, sobre todo, sólo tener en cuenta este
parámetro en el caso de las mieles con un sorprendente bajo porcentaje de polen de
cítricos, y después de la evaluación de sus propiedades organolépticas y fisicoquímicas.
4.1.3. Un sistema de lengua electrónica construido con 5 metales: 4 metales no nobles
(cobre, plata, níquel y cobalto) y únicamente oro como metal noble, es capaz de
diferenciar no sólo entre tipos de miel (azahar, romero, tomillo, girasol, ajedrea y
mielada), sino también predecir su capacidad antioxidante total.
Aunque el origen botánico de las mieles tiene un claro impacto en algunos de los
parámetros químicos y fisicoquímicos estudiados (especialmente el color, la
conductividad eléctrica y la actividad antioxidante), en términos generales con estos
parámetros no se ha logrado una buena diferenciación entre todos los tipos de mieles
estudiadas.
La combinación de la información generada por la lengua electrónica junto con la
aplicación de adecuadas técnicas estadísticas multivariantes, como el PCA y las redes
neuronales (Neural Network Analysis fuzzy artmap type), ha demostrado que este
sistema permite la diferenciación de mieles según su origen botánico con un porcentaje
de éxito del 100%.
Un modelo de predicción MLR demostró una buena correlación entre la lengua
electrónica y la capacidad antioxidante de las mieles (0.9666). Esta correlación fue peor
para otros parámetros evaluados como la conductividad eléctrica (0.8959) e incluso
menor para aw, humedad y color.
Conclusiones
173
En definitiva, el sistema de medición de lengua electrónica propuesto podría ser una
opción rápida y fácil para el sector de envasado de la miel, ya que puede proporcionar
información rápida sobre el origen botánico de una miel e incluso sobre una característica
tan importante como es su capacidad antioxidante.
4.1.4. Se ha estudiado la influencia que tienen determinados parámetros físico-químicos
(HMF, actividad diastásica, humedad, conductividad eléctrica), color (escala Pfund y CIEL
a* b*), azúcares mayoritarios (glucosa, fructosa y sacarosa) y fracción volátil, sobre el
origen geográfico (España, Rumania y República Checa) así como el origen botánico de
mieles de acacia, girasol y tilo. Un análisis de componentes principales (PCA) mostró que
la variedad tiene mayor influencia en la diferenciación de las mieles que el origen
geográfico, debido especialmente a ciertos compuestos volátiles tales como carvacrol y
-terpineno para la miel de tilo; -pineno y 3-methyl-2 butanol para la miel de girasol, y
óxido de cis-linalool para la miel de acacia.
Un análisis discriminante obtenido para cada variedad permitió diferenciar las mieles de
acuerdo a su país de origen, en base principalmente a los compuestos volátiles (2-metil-
2-butenal, 2-metil-2-propanol, éster butílico del ácido acético, etc., para mieles de acacia;
1-hexanol, -pineno, etc., para mieles de girasol y 3-metil-1-butanol, hotrienol, 2-
butanona, etc., para la miel tilo) y, en menor medida a ciertos parámetros fisicoquímicos
tales como, diastasa, sacarosa y conductividad, respectivamente. Los resultados sugieren
que los modelos multivariantes presentados son herramientas potencialmente útiles
para la clasificación de mieles ya que pueden complementar la información obtenida con
el análisis polínico.
4.2.-Conclusiones del objetivo general 2
Aportaciones:
-Food Control (2015), 50, 243-249.
-Food Control (En revisión).
4.2.1. Un análisis multi-residuos de sulfamidas (por LC-MS/MS) fue desarrollado, como
ejemplo, para evaluar la eficacia del control que de forma rutinaria, llevan a cabo las
empresas envasadoras sobre la materia prima, en relación a la presencia de residuos
químicos. Siguiendo los requisitos del correspondiente Reglamento Europeo, se validaron
11 sufamidas (sulfanilamide, sulfathiazole, sulfamerazine, sulfadiazine, sulfapyridine,
sulfamethazine, sulfamethizole, sulfachloropyridazine, sulfamethoxazole,
sulfadimethoxine y sulfaquinoxaline). Se analizaron muestras comercializadas (101)
Conclusiones
174
procedentes de diferentes empresas, así como lotes de materia prima (178 muestras
crudas) de las mismas empresas después de la recepción y antes de su aceptación para
la entrada en el proceso industrial. Todas las muestras comerciales dieron negativo a la
presencia de sulfamidas, sin embargo, de los 178 lotes crudos analizados, en 9 de ellos
se encontró sulfathiazole (3.4% de los casos) y en 3 de ellos sulfadiazine (1.7 % de los
casos). Los resultados confirman que la aplicación en la recepción de un control de
calidad apropiado aplicando una metodología analítica adecuada y validada, resulta
eficaz para reducir en la miel comercializada el riesgo de exposición por la presencia de
sulfonamidas. Este estudio concluye que está garantizada la seguridad del consumidor de
miel en lo que respecta no solo a la presencia de sulfamidas, sino también de otras
sustancias químicas como antibióticos y pesticidas, ya que el control que las empresas
llevan a cabo de forma rutinaria los engloba a todos.
4.2.2. Un análisis de multi-residuos de pesticidas (por LC-MS/MS) fue desarrollado para
evaluar el riesgo de exposición a pesticidas a través del consumo de miel. Siguiendo los
requisitos de la guía SANCO, se validaron (en un rango comprendido entre 1 y 400 g/kg)
11 pesticidas (chlorfenvinphos, coumaphos, tau-fluvalinate, amitraz, muy comunes en
tratamientos veterinarios y imidacloprid, acetamiprid, simazine, cyproconazole,
tebuconazole, chlorpiryphos-methyl, chlorpiryphos, ampliamente utilizados en las
prácticas agrícolas), y 2 metabolitos del amitraz (2,4-DMA and 2,4-DMF). Se evaluaron 22
marcas blancas y primeras marcas del mercado, por representar la casi totalidad de la
miel que se comercializa en España. De este trabajo se concluye que:
-No se superó el LMR para ninguno de los pesticidas analizados, sin embargo, todas
las muestras contenían residuos de alguno de ellos en diferentes concentraciones.
Los pesticidas más abundantes fueron los correspondientes a los tratamientos
veterinarios contra la varroa: amitraz y coumaphos; el primero presente en el 100%
de las muestras y el segundo en el 63% de las mismas. Los pesticidas procedentes
de los tratamientos agrícolas, como acetamiprid y simazine, solo se encontraron en
pocos casos y en concentraciones muy bajas.
-La medida del riesgo potencial para la salud de los consumidores, debido a la
presencia de cantidades de residuos de pesticidas inferiores al LMR, se estimó en
las diferentes muestras comerciales mediante el cálculo del “hazar index” (HI),
como sumatorio de "hazard quotient (HQ)”individual para cada pesticida presente
en ellas. Los valores de HI obtenidos para todas las mieles analizadas fueron en el
peor de los casos 500 veces inferior al valor de 1, considerado como límite de
aceptabilidad.
Conclusiones
175
-La presencia de residuos de pesticidas no sigue un comportamiento geográfico, ya que
un mismo país puede asociarse con muestras con altos valores de HI o con muestras con
bajos valores de HI. Esto hace pensar que la presencia de pesticidas puede estar asociada,
más bien, a las prácticas apícolas, especialmente aquellas que se llevan a cabo contra el
ácaro varroa.
-Aunque los consumidores no están expuestos a niveles tóxicos de pesticidas a través del
consumo de miel, sin embargo, siguiendo el principio de que una exposición tiene que
ser “tan baja como sea razonablemente posible”, el sector primario, apicultores y
agricultores, deben hacer un mayor esfuerzo ya que sus prácticas pueden influir
directamente en el problema de la presencia de residuos en la miel.
Conclusiones
176
Conclusión General de la Tesis
La presente tesis doctoral pone en evidencia que, las técnicas alternativas ensayadas en
el presente estudio para la clasificación industrial de mieles (compuestos volátiles,
lenguas electrónicas) son esperanzadoras, ya que han demostrado ser útiles para
complementar los resultados obtenidos mediante métodos convencionales:
melisopalinológico, fisicoquímicos y sensorial.
Este trabajo demuestra el papel fundamental que juegan los diferentes eslabones de la
cadena de producción de miel (apicultor e industria de envasado), en la obtención del
producto que el consumidor espera y la legislación exige, atendiendo a criterios de
calidad e inocuidad. Para poder garantizar la inocuidad del producto, la industria debe
llevar a cabo de forma rutinaria controles eficaces de la materia prima, especialmente en
lo que respecta a los residuos químicos que pudieran afectar la salud del consumidor. Por
su parte el sector primario, tanto apicultores como agricultores, deben hacer un mayor
esfuerzo, ya que sus prácticas influyen directamente en el problema de la presencia de
residuos en la miel.
